Command A
Language modelActive
Command A
Cohere
- Released
- March 2025
- Data date
- September 8, 2026
- Predecessor
- Command R+ 08-2024 (Official documentation)
- Benchmark-Meta-Score
- Standard error ± 0
- Speed
- 65.8 tokens/sOutput throughput
- Price
- Input · Output / 1M tokens
- Input
- Text
- Output
- Text
Command A combines 23 languages with up to 256,000 context tokens and an explicitly text-based focus on RAG and tool use. Cohere names structured output and multi-step agents as core uses of the checkpoint.
Cohere currently lists command-a-03-2025 as live. The model card sets maximum output at 8,000 tokens and releases the weights under CC-BY-NC-4.0. Later Command A specialists such as Reasoning, Translate, and Vision each have their own model identity.
- Position
- Rank 90 of 91
- Index score
- 0 / 100
Methodology and source
The LLM comparison hub's selected-panel index averages rank percentiles from 65 documented comparison cohorts. Benchmark meta-scores are shown in the benchmark section.
Benchmark meta-analysis
Overall and area scores
These Gradually indexes place selected published measurements on a 0 to 100 scale. They are not accuracy percentages and do not guarantee results in your application.
Retrieval dates of the sources used 2026-10-15 - 2026-10-19
Overall index
Index below 1 / 100
provisional
Estimated without own results in: Agents
Published configurations differ within this comparison cohort. Treat the index as provisional.
- Standard error
- ± 0.0
- 95% interval
- 0.0 to 0.0
- Test series
- 12
- Test families
- 12 / 65
- Sources
- 2
- Share of the fixed test panel (informative)
- 19 %
Coding
Index below 1 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
- Standard error
- ± 0.0
- 95% interval
- 0.0 to 0.0
- Test series
- 3
- Test families
- 3 / 15
- Sources
- 1
- Share of the fixed test panel (informative)
- 11 %
Reasoning
Index below 1 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
- Standard error
- ± 0.0
- 95% interval
- 0.0 to 0.0
- Test series
- 1
- Test families
- 1 / 7
- Sources
- 1
- Share of the fixed test panel (informative)
- 7 %
Mathematics
Index below 1 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
- Standard error
- ± 0.0
- 95% interval
- 0.0 to 0.0
- Test series
- 2
- Test families
- 2 / 5
- Sources
- 1
- Share of the fixed test panel (informative)
- 40 %
Knowledge
Index below 1 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
- Standard error
- ± 0.0
- 95% interval
- 0.0 to 0.0
- Test series
- 1
- Test families
- 1 / 3
- Sources
- 1
- Share of the fixed test panel (informative)
- 33 %
General
Index below 1 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
- Standard error
- ± 0.0
- 95% interval
- 0.0 to 0.0
- Test series
- 1
- Test families
- 1 / 5
- Sources
- 1
- Share of the fixed test panel (informative)
- 20 %
Finance
Index below 1 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
- Standard error
- ± 0.0
- 95% interval
- 0.0 to 0.0
- Test series
- 2
- Test families
- 2 / 7
- Sources
- 1
- Share of the fixed test panel (informative)
- 29 %
Legal
Index below 1 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
- Standard error
- ± 0.0
- 95% interval
- 0.0 to 0.0
- Test series
- 1
- Test families
- 1 / 4
- Sources
- 1
- Share of the fixed test panel (informative)
- 25 %
Health
Index below 1 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
- Standard error
- ± 0.0
- 95% interval
- 0.0 to 0.0
- Test series
- 1
- Test families
- 1 / 3
- Sources
- 1
- Share of the fixed test panel (informative)
- 33 %
Documented method · Included test families and measurements
Method version: benchmark-meta-score-v3
The index includes active measurements from reviewed primary sources when at least five models are compared in the same documented comparison slice across dataset, harness, agent, tools, and trial count. Each benchmark series and evaluator contributes one shared setup slice from the newest capture. When one source and series include several explicitly documented reasoning tiers, the highest documented tier (none through max) is selected, never the best result value. The explicit source label “Non-reasoning” is treated as none. Unknown or ambiguous tiers exclude the model from that series. Within each series, percentages and rates are logit-transformed, other units stay unchanged, and all values are then standardized. A one-factor model then estimates one shared strength per model and, per series, how sharply it separates the models. Area scores move away from that strength only as far as the model's own results in the area support. The overall score weights the core areas at 75% and finance, law and health at 25%. It requires at least eight test series from two sources and three areas. The 0 to 100 scale refers to a fixed reference group of 29 language models whose average sits at 50.
Published model-specific parameters can differ. This is not a controlled experiment; comparisons with such differences are marked provisional.
Unmeasured series are not entered as zero. Where a model has no own results in a core area, its overall strength stands in, and the note “estimated without own results in …” names the area. Fewer test series lead to a larger standard error.
The 95% interval follows from the standard error of 200 recalculations with randomly redrawn test families. It describes how precisely the recorded test series determine the value. It is no prediction for future tasks or for later recalculations with other participants.
The source count is not a count of independent experiments.
OECD handbook on composite indicatorsAll 12 test families and 12 of 12 raw measurements are listed here. The complete calculation for every model is available in the JSON export.
Test family: IFBench
General · Mean rank percentile of the family: 20.3 / 100Family weight in the fixed test panel (informative): 2.14 % · Coverage of this test family: 100 %
- Artificial Analysis
Published configurations differ within this cohort.
- Measurement:
- artificial-analysis-ifbench
- Series weight within the test family:
- 100 %
- Nominal contribution to the overall panel:
- 2.14 %
- Raw value:
- 36.4625850340136 %
- Metric:
- IFBench prompt-level loose accuracy (pass@1)
- Version:
- AA test set: 294 questions
- Participating models:
- 70
- Rank percentile within the cohort:
- 20.3 %
- Retrieved:
- 2026-10-19
- Comparison cohort:
- artificial-analysis-ifbench:AA test set: 294 questions:standalone:non-reasoning:tokens=0:suffix=unspecified
- Documented comparison setup
- Publication cohort:
- ["artificial-analysis-ifbench","AA test set: 294 questions","aa-model-page-2026-10-19-8a022369738c"]
- Dataset type:
- artificial-analysis-evaluation
- Tool policy:
- Single-turn answer evaluated with the official IFBench source code
- Attempts:
- pass@1, 5 repeats per question
- Setup and model parameters
- Exact AA configuration: Command A (release command-a, row command-a, UUID 3bc32f13-5afa-4e28-bce1-10e57376686b, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning
Exact AA configuration: Command A (release command-a, row command-a, UUID 3bc32f13-5afa-4e28-bce1-10e57376686b, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Source model ID: artificial-analysis/3bc32f13-5afa-4e28-bce1-10e57376686b/command-a/command-a. Source model name: Command A. Provider reported by source: Cohere. Model snapshot: Command A. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning. Score display: 36.5%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
- Artificial Analysis
Test family: GPQA Diamond
Reasoning · Mean rank percentile of the family: 7.9 / 100Family weight in the fixed test panel (informative): 1.53 % · Coverage of this test family: 50 %
- Vals AI
Published configurations differ within this cohort.
- Measurement:
- vals-gpqa
- Series weight within the test family:
- 50 %
- Nominal contribution to the overall panel:
- 0.77 %
- Raw value:
- 48.485 %
- Metric:
- accuracy
- Version:
- 1
- Participating models:
- 77
- Rank percentile within the cohort:
- 7.9 %
- Retrieved:
- 2026-10-15
- Comparison cohort:
- vals-gpqa:1:overall
- Documented comparison setup
- Publication cohort:
- vals-gpqa:1:overall
- Dataset type:
- public
- Setup and model parameters
- Temperature: 0.3. Maximum output tokens: 8000
Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 11.49. Standard error: 2.276. Cost per task in USD: 0.010094. Source ID: vals-ai. Evidence capture ID: vals-gpqa-2026-10-15-951eb5224097. Source payload hash: 951eb5224097733a9d09d06c14908ae2980e2e640e539f2c284877c5092d7077. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Vals AI
Test family: SWE-bench Verified
Coding · Mean rank percentile of the family: 0.0 / 100Family weight in the fixed test panel (informative): 0.71 % · Coverage of this test family: 50 %
- Vals AI
Published configurations differ within this cohort.
- Measurement:
- vals-swebench
- Series weight within the test family:
- 50 %
- Nominal contribution to the overall panel:
- 0.36 %
- Raw value:
- 7.8 %
- Metric:
- accuracy
- Version:
- 1
- Participating models:
- 60
- Rank percentile within the cohort:
- 0.0 %
- Retrieved:
- 2026-10-15
- Comparison cohort:
- vals-swebench:1:overall
- Documented comparison setup
- Publication cohort:
- vals-swebench:1:overall
- Dataset type:
- public
- Setup and model parameters
- Temperature: 0.3. Maximum output tokens: 8000
Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 227.531. Standard error: 1.201. Cost per task in USD: 0.21024. Source ID: vals-ai. Evidence capture ID: vals-swebench-2026-10-15-7a4e2e848528. Source payload hash: 7a4e2e8485283caac500127419f68ec4bd5414c837ade114fc6afa5b2d34ccaa. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Vals AI
Test family: Terminal-Bench
Coding · Mean rank percentile of the family: 0.0 / 100Family weight in the fixed test panel (informative): 0.71 % · Coverage of this test family: 17 %
- Vals AI
Published configurations differ within this cohort.
- Measurement:
- vals-terminal-bench-2
- Series weight within the test family:
- 16.67 %
- Nominal contribution to the overall panel:
- 0.12 %
- Raw value:
- 2.247 %
- Metric:
- accuracy
- Version:
- 2.0
- Participating models:
- 43
- Rank percentile within the cohort:
- 0.0 %
- Retrieved:
- 2026-10-15
- Comparison cohort:
- vals-terminal-bench-2:2.0:overall
- Documented comparison setup
- Publication cohort:
- vals-terminal-bench-2:2.0:overall
- Dataset type:
- public
- Setup and model parameters
- Temperature: 0.3. Maximum output tokens: 8000
Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-06-04. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 760.674. Standard error: 1.58. Cost per task in USD: 2.436871. Source ID: vals-ai. Evidence capture ID: vals-terminal-bench-2-2026-10-15-38e640d99525. Source payload hash: 38e640d995259778e531f048b2f2c7b3b281cb05b56be643ddc9dc53eb85adf7. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Vals AI
Test family: CorpFin v2
Finance · Mean rank percentile of the family: 5.4 / 100Family weight in the fixed test panel (informative): 1.19 % · Coverage of this test family: 100 %
- Vals AI
Published configurations differ within this cohort.
- Measurement:
- vals-corp-fin-v2
- Series weight within the test family:
- 100 %
- Nominal contribution to the overall panel:
- 1.19 %
- Raw value:
- 45.96 %
- Metric:
- accuracy
- Version:
- 2
- Participating models:
- 75
- Rank percentile within the cohort:
- 5.4 %
- Retrieved:
- 2026-10-15
- Comparison cohort:
- vals-corp-fin-v2:2:overall
- Documented comparison setup
- Publication cohort:
- vals-corp-fin-v2:2:overall
- Dataset type:
- private
- Setup and model parameters
- Temperature: 0.3. Maximum output tokens: 8000
Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 14.146. Standard error: 0.972. Cost per task in USD: 0.206138. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-10-15-7cf2217ab372. Source payload hash: 7cf2217ab3728d5a84bffce82aab9fc52d7f810b0cbe8c65b387df47f3d31c9b. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Vals AI
Test family: LiveCodeBench v6
Coding · Mean rank percentile of the family: 3.9 / 100Family weight in the fixed test panel (informative): 0.71 % · Coverage of this test family: 100 %
- Vals AI
Published configurations differ within this cohort.
- Measurement:
- vals-lcb
- Series weight within the test family:
- 100 %
- Nominal contribution to the overall panel:
- 0.71 %
- Raw value:
- 35.071 %
- Metric:
- accuracy
- Version:
- 1
- Participating models:
- 78
- Rank percentile within the cohort:
- 3.9 %
- Retrieved:
- 2026-10-15
- Comparison cohort:
- vals-lcb:1:overall
- Documented comparison setup
- Publication cohort:
- vals-lcb:1:overall
- Dataset type:
- public
- Setup and model parameters
- Temperature: 0.3. Maximum output tokens: 8000
Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 11.746. Standard error: 1.086. Cost per task in USD: 0.006568. Source ID: vals-ai. Evidence capture ID: vals-lcb-2026-10-15-1a7c7dabc9d9. Source payload hash: 1a7c7dabc9d95a0182886b1edc42623987cac2095a0d407a5e334f37acdfa2f5. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Vals AI
Test family: LegalBench
Legal · Mean rank percentile of the family: 30.8 / 100Family weight in the fixed test panel (informative): 2.08 % · Coverage of this test family: 100 %
- Vals AI
Published configurations differ within this cohort.
- Measurement:
- vals-legal-bench
- Series weight within the test family:
- 100 %
- Nominal contribution to the overall panel:
- 2.08 %
- Raw value:
- 79.697 %
- Metric:
- accuracy
- Version:
- 1
- Participating models:
- 79
- Rank percentile within the cohort:
- 30.8 %
- Retrieved:
- 2026-10-15
- Comparison cohort:
- vals-legal-bench:1:overall
- Documented comparison setup
- Publication cohort:
- vals-legal-bench:1:overall
- Dataset type:
- public
- Setup and model parameters
- Temperature: 0.3. Maximum output tokens: 8000
Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-10-13. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 0.797. Standard error: 0.422. Source ID: vals-ai. Evidence capture ID: vals-legal_bench-2026-10-15-c8486ba756ba. Source payload hash: c8486ba756baa5cd3955d035edff67440e8db23e69d62ad1a9e18d15b8c4bc52. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Vals AI
Test family: MATH 500
Mathematics · Mean rank percentile of the family: 13.6 / 100Family weight in the fixed test panel (informative): 2.14 % · Coverage of this test family: 100 %
- Vals AI
Published configurations differ within this cohort.
- Measurement:
- vals-math500
- Series weight within the test family:
- 100 %
- Nominal contribution to the overall panel:
- 2.14 %
- Raw value:
- 76.2 %
- Metric:
- accuracy
- Version:
- 1
- Participating models:
- 23
- Rank percentile within the cohort:
- 13.6 %
- Retrieved:
- 2026-10-15
- Comparison cohort:
- vals-math500:1:overall
- Documented comparison setup
- Publication cohort:
- vals-math500:1:overall
- Dataset type:
- public
- Setup and model parameters
- Temperature: 0. Maximum output tokens: 8192
Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-01-09. Temperature: 0. Maximum output tokens: 8192. Latency in seconds: 8.665. Standard error: 1.904. Cost per task in USD: 0.005983. Source ID: vals-ai. Evidence capture ID: vals-math500-2026-10-15-435ec4da1112. Source payload hash: 435ec4da1112362c0a6bb4afeab53bf7b0faf2000e1ddbe1a878735dca1fe35b. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Vals AI
Test family: MedQA
Health · Mean rank percentile of the family: 17.9 / 100Family weight in the fixed test panel (informative): 2.78 % · Coverage of this test family: 100 %
- Vals AI
Published configurations differ within this cohort.
- Measurement:
- vals-medqa
- Series weight within the test family:
- 100 %
- Nominal contribution to the overall panel:
- 2.78 %
- Raw value:
- 80.55 %
- Metric:
- accuracy
- Version:
- 1
- Participating models:
- 40
- Rank percentile within the cohort:
- 17.9 %
- Retrieved:
- 2026-10-15
- Comparison cohort:
- vals-medqa:1:overall
- Documented comparison setup
- Publication cohort:
- vals-medqa:1:overall
- Dataset type:
- public
- Setup and model parameters
- Temperature: 0.3. Maximum output tokens: 8000
Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 2.908. Standard error: 0.361. Cost per task in USD: 0.003459. Source ID: vals-ai. Evidence capture ID: vals-medqa-2026-10-15-d67905978d93. Source payload hash: d67905978d932f64698ebe9e230d9519e5bae1c9d7176dda8379dc3874b6f90f. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Vals AI
Test family: MGSM
Mathematics · Mean rank percentile of the family: 10.3 / 100Family weight in the fixed test panel (informative): 2.14 % · Coverage of this test family: 100 %
- Vals AI
Published configurations differ within this cohort.
- Measurement:
- vals-mgsm
- Series weight within the test family:
- 100 %
- Nominal contribution to the overall panel:
- 2.14 %
- Raw value:
- 85.709 %
- Metric:
- accuracy
- Version:
- 1
- Participating models:
- 30
- Rank percentile within the cohort:
- 10.3 %
- Retrieved:
- 2026-10-15
- Comparison cohort:
- vals-mgsm:1:overall
- Documented comparison setup
- Publication cohort:
- vals-mgsm:1:overall
- Dataset type:
- public
- Setup and model parameters
- Temperature: 0.3. Maximum output tokens: 8000
Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-01-09. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 8.36. Standard error: 2.016. Cost per task in USD: 0.003732. Source ID: vals-ai. Evidence capture ID: vals-mgsm-2026-10-15-1b87f953e52c. Source payload hash: 1b87f953e52c00bf4bdd90c3add26165f644eeb7a74a8382d2a0324ff0cc1040. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Vals AI
Test family: MMLU-Pro
Knowledge · Mean rank percentile of the family: 5.2 / 100Family weight in the fixed test panel (informative): 3.57 % · Coverage of this test family: 100 %
- Vals AI
Published configurations differ within this cohort.
- Measurement:
- vals-mmlu-pro
- Series weight within the test family:
- 100 %
- Nominal contribution to the overall panel:
- 3.57 %
- Raw value:
- 69.172 %
- Metric:
- accuracy
- Version:
- 1
- Participating models:
- 78
- Rank percentile within the cohort:
- 5.2 %
- Retrieved:
- 2026-10-15
- Comparison cohort:
- vals-mmlu-pro:1:overall
- Documented comparison setup
- Publication cohort:
- vals-mmlu-pro:1:overall
- Dataset type:
- public
- Setup and model parameters
- Temperature: 0.3. Maximum output tokens: 8000
Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 9.853. Standard error: 0.463. Cost per task in USD: 0.007902. Source ID: vals-ai. Evidence capture ID: vals-mmlu_pro-2026-10-15-dab025507f46. Source payload hash: dab025507f46c716dd8d145e90ec0a737d9c56219853f41e4b1cc3c5e8c222b4. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Vals AI
Test family: TaxEval v2
Finance · Mean rank percentile of the family: 5.3 / 100Family weight in the fixed test panel (informative): 1.19 % · Coverage of this test family: 100 %
- Vals AI
Published configurations differ within this cohort.
- Measurement:
- vals-tax-eval-v2
- Series weight within the test family:
- 100 %
- Nominal contribution to the overall panel:
- 1.19 %
- Raw value:
- 61.366 %
- Metric:
- accuracy
- Version:
- 2
- Participating models:
- 77
- Rank percentile within the cohort:
- 5.3 %
- Retrieved:
- 2026-10-15
- Comparison cohort:
- vals-tax-eval-v2:2:overall
- Documented comparison setup
- Publication cohort:
- vals-tax-eval-v2:2:overall
- Dataset type:
- private
- Setup and model parameters
- Temperature: 0.3. Maximum output tokens: 8000
Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 11.214. Standard error: 0.952. Cost per task in USD: 0.006484. Source ID: vals-ai. Evidence capture ID: vals-tax_eval_v2-2026-10-15-8b354a6ae848. Source payload hash: 8b354a6ae848f5bb25a2e8b061b5f576d5b9e0c11b941dc1e6bed078e6529cd6. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Vals AI
Benchmark sources at a glance
Each evaluator measures different capabilities under its own conditions. Test series, composite indices and their components stay separate. The detailed results below retain their scales, configurations and source dates.
| Evaluator | Test series | Composite indices | Recorded active measurements |
|---|---|---|---|
| Artificial Analysis | 2 | 1 | 11 |
| Vals AI | 13 | 0 | 68 |
Coverage is not a quality ranking. Overlapping tests and multiple settings do not count as independent experiments. Vendor claims, historical and disputed results are excluded from this overview and remain labelled in the detailed evidence.
6 measurements from these sources still lack a verified test-family identity and do not increase the test-series count.
Page 1 of 9
Published benchmarks
MATH 500
Overall
higher is betterAs of October 15, 2026Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-math500:1:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 5
- Methodology and context
- Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-01-09. Temperature: 0. Maximum output tokens: 8192. Latency in seconds: 8.665. Standard error: 1.904. Cost per task in USD: 0.005983. Source ID: vals-ai. Evidence capture ID: vals-math500-2026-10-15-435ec4da1112. Source payload hash: 435ec4da1112362c0a6bb4afeab53bf7b0faf2000e1ddbe1a878735dca1fe35b. Parser version: vals-astro-v3. Imported at: 2026-10-15
Measurements without matching peer values
These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.
IFBench: Overall (Show measurement, test conditions, and source)
- Source value
- 36.4625850340136
- Score
- 36.4625850340136
- Metric
- IFBench prompt-level loose accuracy (pass@1)
- Unit
- %
- Benchmark version
- AA test set: 294 questions
- Category
- general
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- artificial-analysis-ifbench:AA test set: 294 questions:standalone:non-reasoning:tokens=0:suffix=unspecified
- Source type
- independent-evaluator
- Evaluator
- Artificial Analysis
- Status
- active
- Retrieved at
- 2026-10-19
- Methodology
- Exact AA configuration: Command A (release command-a, row command-a, UUID 3bc32f13-5afa-4e28-bce1-10e57376686b, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Source model ID: artificial-analysis/3bc32f13-5afa-4e28-bce1-10e57376686b/command-a/command-a. Source model name: Command A. Provider reported by source: Cohere. Model snapshot: Command A. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning. Score display: 36.5%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
- Comparison methodology
- Exact AA configuration: Command A (release command-a, row command-a, UUID 3bc32f13-5afa-4e28-bce1-10e57376686b, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning
- Context
- The often cited 58 refers to verifiable out-of-domain constraint types, not the number of questions in AA's test run.
AA-Omniscience Accuracy: Overall (Show measurement, test conditions, and source)
- Source value
- 16.383333333333333
- Score
- 16.383333333333333
- Metric
- AA-Omniscience Accuracy
- Unit
- %
- Benchmark version
- AA Omniscience diagnostic
- Category
- knowledge
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- artificial-analysis-omniscience-accuracy:AA Omniscience diagnostic:standalone:non-reasoning:tokens=0:suffix=unspecified
- Source type
- independent-evaluator
- Evaluator
- Artificial Analysis
- Status
- active
- Retrieved at
- 2026-10-19
- Methodology
- Exact AA configuration: Command A (release command-a, row command-a, UUID 3bc32f13-5afa-4e28-bce1-10e57376686b, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. Proportion of all questions answered correctly, regardless of whether the model answers or abstains on other questions.. Source model ID: artificial-analysis/3bc32f13-5afa-4e28-bce1-10e57376686b/command-a/command-a. Source model name: Command A. Provider reported by source: Cohere. Model snapshot: Command A. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Attempts: AA-reported aggregate; diagnostic-specific run count is not stated on the leaderboard. Reasoning: non-reasoning. Score display: 16.4%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
- Comparison methodology
- Exact AA configuration: Command A (release command-a, row command-a, UUID 3bc32f13-5afa-4e28-bce1-10e57376686b, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. Proportion of all questions answered correctly, regardless of whether the model answers or abstains on other questions.. Attempts: AA-reported aggregate; diagnostic-specific run count is not stated on the leaderboard. Reasoning: non-reasoning
- Context
- This is the separately published accuracy diagnostic, not the AA-Omniscience Index. AA identifies higher values as better.
AA-Omniscience Hallucination Rate: Overall (Show measurement, test conditions, and source)
- Source value
- 77.31712178592784
- Score
- 77.31712178592784
- Metric
- AA-Omniscience Hallucination Rate
- Unit
- %
- Benchmark version
- AA Omniscience diagnostic
- Category
- knowledge
- Task
- overall
- Task label
- Overall
- Direction
- Lower is better
- Comparison cohort
- artificial-analysis-omniscience-hallucination-rate:AA Omniscience diagnostic:standalone:non-reasoning:tokens=0:suffix=unspecified
- Source type
- independent-evaluator
- Evaluator
- Artificial Analysis
- Status
- active
- Retrieved at
- 2026-10-19
- Methodology
- Exact AA configuration: Command A (release command-a, row command-a, UUID 3bc32f13-5afa-4e28-bce1-10e57376686b, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. Proportion of incorrect answers among all non-correct responses: incorrect / (incorrect + partial answers + not attempted).. Source model ID: artificial-analysis/3bc32f13-5afa-4e28-bce1-10e57376686b/command-a/command-a. Source model name: Command A. Provider reported by source: Cohere. Model snapshot: Command A. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Attempts: AA-reported aggregate; diagnostic-specific run count is not stated on the leaderboard. Reasoning: non-reasoning. Score display: 77.3%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
- Comparison methodology
- Exact AA configuration: Command A (release command-a, row command-a, UUID 3bc32f13-5afa-4e28-bce1-10e57376686b, reasoning=false, source reasoning-token configuration budget=0, suffix=not specified). This configured value is not measured token consumption. Proportion of incorrect answers among all non-correct responses: incorrect / (incorrect + partial answers + not attempted).. Attempts: AA-reported aggregate; diagnostic-specific run count is not stated on the leaderboard. Reasoning: non-reasoning
- Context
- This is the separately published hallucination-rate diagnostic, not the AA-Omniscience Index. AA identifies lower values as better.
AIME: AIME 2024 (Show measurement, test conditions, and source)
- Source value
- 15
- Score
- 15
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1
- Category
- math
- Task
- aime_2024
- Task label
- AIME 2024
- Direction
- Higher is better
- Comparison cohort
- vals-aime:1:aime_2024
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-10-15
- Methodology
- Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Temperature: 0.3. Latency in seconds: 23.494. Standard error: 1.543. Cost per task in USD: 0.012986. Source ID: vals-ai. Evidence capture ID: vals-aime-2026-10-15-7d016210b39d. Source payload hash: 7d016210b39d24351bbb5d12be09a973decac461bb2486eb1f23724fed2b7e6c. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Comparison methodology
- Temperature: 0.3
- Context
- The source dataset marks this benchmark as archived. The result remains visible as historical evidence and is not interchangeable with an active leaderboard.
AIME: AIME 2025 (Show measurement, test conditions, and source)
- Source value
- 11.667
- Score
- 11.667
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1
- Category
- math
- Task
- aime_2025
- Task label
- AIME 2025
- Direction
- Higher is better
- Comparison cohort
- vals-aime:1:aime_2025
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-10-15
- Methodology
- Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Temperature: 0.3. Latency in seconds: 23.196. Standard error: 1.091. Cost per task in USD: 0.010479. Source ID: vals-ai. Evidence capture ID: vals-aime-2026-10-15-7d016210b39d. Source payload hash: 7d016210b39d24351bbb5d12be09a973decac461bb2486eb1f23724fed2b7e6c. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Comparison methodology
- Temperature: 0.3
- Context
- The source dataset marks this benchmark as archived. The result remains visible as historical evidence and is not interchangeable with an active leaderboard.
AIME: Overall (Show measurement, test conditions, and source)
- Source value
- 13.333
- Score
- 13.333
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1
- Category
- math
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-aime:1:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-10-15
- Methodology
- Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Temperature: 0.3. Latency in seconds: 23.345. Standard error: 0.945. Cost per task in USD: 0.011732. Source ID: vals-ai. Evidence capture ID: vals-aime-2026-10-15-7d016210b39d. Source payload hash: 7d016210b39d24351bbb5d12be09a973decac461bb2486eb1f23724fed2b7e6c. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Comparison methodology
- Temperature: 0.3
- Context
- The source dataset marks this benchmark as archived. The result remains visible as historical evidence and is not interchangeable with an active leaderboard.
CaseLaw v2: Overall (Show measurement, test conditions, and source)
- Source value
- 64.523
- Score
- 64.523
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 2
- Category
- legal
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-case-law-v2:2:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-10-15
- Methodology
- Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-05-04. Temperature: 1. Maximum output tokens: 8192. Latency in seconds: 41.555. Standard error: 1.088. Cost per task in USD: 0.098244. Source ID: vals-ai. Evidence capture ID: vals-case_law_v2-2026-10-15-293f388f7e0f. Source payload hash: 293f388f7e0fe563287d4cd19beaa7ef9821b32795dace34bd0197ed47a592d4. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Comparison methodology
- Temperature: 1. Maximum output tokens: 8192
- Context
- The source dataset marks this benchmark as archived. The result remains visible as historical evidence and is not interchangeable with an active leaderboard.
CorpFin v2: Exact Pages (Show measurement, test conditions, and source)
- Source value
- 56.177
- Score
- 56.177
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 2
- Category
- finance
- Task
- exact_pages
- Task label
- Exact Pages
- Direction
- Higher is better
- Comparison cohort
- vals-corp-fin-v2:2:exact_pages
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-10-15
- Methodology
- Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 3.828. Standard error: 1.694. Cost per task in USD: 0.005637. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-10-15-7cf2217ab372. Source payload hash: 7cf2217ab3728d5a84bffce82aab9fc52d7f810b0cbe8c65b387df47f3d31c9b. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Comparison methodology
- Temperature: 0.3. Maximum output tokens: 8000
CorpFin v2: Max Fitting Context (Show measurement, test conditions, and source)
- Source value
- 41.958
- Score
- 41.958
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 2
- Category
- finance
- Task
- max_fitting_context
- Task label
- Max Fitting Context
- Direction
- Higher is better
- Comparison cohort
- vals-corp-fin-v2:2:max_fitting_context
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-10-15
- Methodology
- Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 27.747. Standard error: 1.685. Cost per task in USD: 0.445899. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-10-15-7cf2217ab372. Source payload hash: 7cf2217ab3728d5a84bffce82aab9fc52d7f810b0cbe8c65b387df47f3d31c9b. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Comparison methodology
- Temperature: 0.3. Maximum output tokens: 8000
CorpFin v2: Overall (Show measurement, test conditions, and source)
- Source value
- 45.96
- Score
- 45.96
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 2
- Category
- finance
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-corp-fin-v2:2:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-10-15
- Methodology
- Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 14.146. Standard error: 0.972. Cost per task in USD: 0.206138. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-10-15-7cf2217ab372. Source payload hash: 7cf2217ab3728d5a84bffce82aab9fc52d7f810b0cbe8c65b387df47f3d31c9b. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Comparison methodology
- Temperature: 0.3. Maximum output tokens: 8000
CorpFin v2: Shared Max Context (Show measurement, test conditions, and source)
- Source value
- 39.744
- Score
- 39.744
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 2
- Category
- finance
- Task
- shared_max_context
- Task label
- Shared Max Context
- Direction
- Higher is better
- Comparison cohort
- vals-corp-fin-v2:2:shared_max_context
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-10-15
- Methodology
- Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Temperature: 0.3. Maximum output tokens: 8000. Latency in seconds: 10.862. Standard error: 1.671. Cost per task in USD: 0.166879. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-10-15-7cf2217ab372. Source payload hash: 7cf2217ab3728d5a84bffce82aab9fc52d7f810b0cbe8c65b387df47f3d31c9b. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Comparison methodology
- Temperature: 0.3. Maximum output tokens: 8000
Recorded measurement details
MATH 500: Overall (Show measurement, test conditions, and source)
- Source value
- 76.2
- Score
- 76.2
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1
- Category
- math
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-math500:1:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-10-15
- Methodology
- Source model ID: cohere/command-a-03-2025. Source model name: Command A. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-01-09. Temperature: 0. Maximum output tokens: 8192. Latency in seconds: 8.665. Standard error: 1.904. Cost per task in USD: 0.005983. Source ID: vals-ai. Evidence capture ID: vals-math500-2026-10-15-435ec4da1112. Source payload hash: 435ec4da1112362c0a6bb4afeab53bf7b0faf2000e1ddbe1a878735dca1fe35b. Parser version: vals-astro-v3. Imported at: 2026-10-15
- Comparison methodology
- Temperature: 0. Maximum output tokens: 8192
Page 1 of 9
Our tests
SVG short test
One SVG prompt and fixed output settings. Captures are shown only with 0 reported reasoning tokens or documented lack of reasoning controls. Identical test conditions do not make the models' internal compute identical.
Earlier tests
Pelican on a bicycle
A community classic for free-form SVG drawing.
Command A
Reasoning (requested): not specified
Current attempt
- Run date
- Oct 2, 2026
- API provider
- openrouter
- Requested API model
- cohere/command-a
- API model reported in response
- cohere/command-a
- Reasoning setting
- not specified
- Pinned endpoint
- Cohere | cohere/command-a-03-2025
- Temperature
- Provider default
- Token limit including reasoning
- 8,192 tokens
- Test protocol
- community-visual-tests-v1
- Technical checks
- Passed, not a quality rating
- Response time
- 12.9 s
Visually reviewed. This individual test does not establish overall model quality.
Prompt and test conditions
Generate an SVG of a pelican riding a bicycle
Token limit including reasoning: 8,192 tokens
Task origin (Simon Willison)Popularity
28.9 / 100
Specifications and access
| Specification | Value and source |
|---|---|
| Weights | Open weightsSource |
| Access | API, LocalSource |
| Parameters | 111BSource |
| Context window | 256,000 tokensSource |
| Knowledge cutoff | June 1, 2024Source |
| Notes | 256K context windowSource |
| License | CC-BY-NC-4.0Source |
| Release | 2025-03-13Source |
| API model ID | command-a-03-2025Source |
| Parameters | 111 BSource |
| Architecture | auto-regressive transformerSource |
| Context window | 256,000 tokens (HF card notes its config is 128K despite the supported 256K context.)Source |
| Maximum output | 8,000 tokensSource |
| Input | TextSource |
| Output | TextSource |
| Languages | 23Source |
| Tool calling | YesSource |
| Structured output | YesSource |
Predecessor and successor
Sources and data date
| Type | Evidence and data date |
|---|---|
| Speed | Cohere. Output speed after the first streaming chunk, not total response time. Retrieved September 8, 2026. |
| Price | Base API prices without caching or batch discounts. Higher context tiers and other rates are listed under Costs. |
| Input | Documented modalities. Retrieved September 8, 2026. |
| Output | Documented modalities. Retrieved September 8, 2026. |
| Research status | Research date September 8, 2026. 5 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication. |
| Not found in the inspected sources | training-tokens Original wording The checked docs, launch post, card and technical report do not publish a total training-token count. |
| Additional source | Artificial Analysis (retrieved October 19, 2026) |
| Additional source | Artificial Analysis (retrieved October 19, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Vals AI (retrieved October 15, 2026) |
| Additional source | Cohere Command A technical report (retrieved September 8, 2026) · Cohere Command A technical report · Editorial description reviewed October 2, 2026 |
| Additional source | Model metadata (retrieved September 8, 2026) |
| Additional source | OpenRouter: Popularity index (derived), CC BY 4.0 |
| Additional source | Vercel AI Gateway: Popularity index (derived), CC BY 4.0 |
| Additional source | Ollama: cumulative downloads for the popularity index |
| Additional source | Replicate: cumulative runs for the popularity index |
| Additional source | Google Trending Now: limited feed selection for Germany and the US |
| Additional source | Creative Commons Attribution 4.0 |