Language modelDeprecated

Command R+

Cohere

Released: April 2024
Data date: September 8, 2026
Successor: Command R+ 08-2024 (Official documentation)

Benchmark-Meta-Score: Unavailable
Fewer than eight test series
Input: Text
Output: Text

Command R+ carries the original Command R approach into a larger model class with 104 billion parameters, ten languages, and a 128,000-token context. Cohere built the checkpoint for more demanding RAG work and multi-step tool use. Its size was the main distinction from the smaller Command R.

Cohere identifies this model as command-r-plus-04-2024. The API alias has been deprecated since September 15, 2025. The command-r-plus-08-2024 update remains a separate checkpoint. The open weights use CC-BY-NC-4.0 and remain subject to the documented usage terms.

Specifications and access

Specification	Value and source
Weights	Open weightsSource
Access	LocalSource
Parameters	104BSource
Context window	128,000 tokensSource
License	CC-BY-NC-4.0Source
Release	2024-04-04Source
Parameters	104 BSource
Architecture	auto-regressive transformerSource
Maximum output	4,000 tokensSource
Input	TextSource
Output	TextSource
Languages	10Source
Tool calling	YesSource
API model ID	command-r-plus-04-2024Source
Model revision release	2024-08-30 (This dates the separate command-r-plus-08-2024 refresh and is not an original Command R+ release date.)Source

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 - 2026-10-15

Overall index

Not enough independent results for a score yet

An overall score needs at least eight test series.

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Test series: 5
Test families: 5 / 65
Sources: 1
Share of the fixed test panel (informative): 10 %

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: 1
Test families: 1 / 15
Sources: 1
Share of the fixed test panel (informative): 7 %

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 %

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 %

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 indicators

All 5 test families and 5 of 5 raw measurements are listed here. The complete calculation for every model is available in the JSON export.

Test family: GPQA Diamond
Reasoning · Mean rank percentile of the family: 0.0 / 100
Family 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:
  31.06 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  77
  Rank percentile within the cohort:
  0.0 %
  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: 4000
  Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 8.194. Standard error: 2.28. Cost per task in USD: 0.007438. 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
Test family: LiveCodeBench v6
Coding · Mean rank percentile of the family: 0.0 / 100
Family 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:
  18.238 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  78
  Rank percentile within the cohort:
  0.0 %
  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: 4000
  Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 5.463. Standard error: 1.016. Cost per task in USD: 0.003267. 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
Test family: LegalBench
Legal · Mean rank percentile of the family: 7.7 / 100
Family 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:
  68.287 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  79
  Rank percentile within the cohort:
  7.7 %
  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: 4000
  Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-10-13. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 0.396. Standard error: 0.65. 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
Test family: MedQA
Health · Mean rank percentile of the family: 0.0 / 100
Family 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:
  2.651 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  40
  Rank percentile within the cohort:
  0.0 %
  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: 4000
  Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 6.056. Standard error: 0.29. Cost per task in USD: 0.003456. 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
Test family: MMLU-Pro
Knowledge · Mean rank percentile of the family: 0.0 / 100
Family 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:
  44.003 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  78
  Rank percentile within the cohort:
  0.0 %
  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: 4000
  Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 4.419. Standard error: 0.479. Cost per task in USD: 0.00566. 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

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
Vals AI	5	0	35

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.

Published benchmarks

Legal

LegalBench

Sub-task	Command R+ (This model)	Command R
Conclusion Tasks	48.31% (best value in this row)	13.98%
Interpretation Tasks	71.62% (best value in this row)	38.72%
Issue Tasks	76.32% (best value in this row)	8.89%
Overall	68.29% (best value in this row)	32.97%
Rhetoric Tasks	75.64% (best value in this row)	71.01%
Rule Tasks	69.55% (best value in this row)	32.24%

higher is betterAs of October 15, 2026Source: Vals AIbars scaled within each row, not across rows

Methodology and context

Metric: accuracy

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.

GPQA Diamond: Few-Shot CoT (Show measurement, test conditions, and source)

Source value: 28.283
Score: 28.283
Metric: accuracy
Unit: %
Benchmark version: 1
Category: reasoning
Task: diamond_few_shot_cot
Task label: Few-Shot CoT
Direction: Higher is better
Comparison cohort: vals-gpqa:1:diamond_few_shot_cot
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 8. Standard error: 3.183. Cost per task in USD: 0.009178. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

GPQA Diamond: Zero-Shot CoT (Show measurement, test conditions, and source)

Source value: 33.838
Score: 33.838
Metric: accuracy
Unit: %
Benchmark version: 1
Category: reasoning
Task: diamond_zero_shot_cot
Task label: Zero-Shot CoT
Direction: Higher is better
Comparison cohort: vals-gpqa:1:diamond_zero_shot_cot
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 8.387. Standard error: 3.266. Cost per task in USD: 0.005699. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

GPQA Diamond: Overall (Show measurement, test conditions, and source)

Source value: 31.06
Score: 31.06
Metric: accuracy
Unit: %
Benchmark version: 1
Category: reasoning
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: vals-gpqa:1:overall
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 8.194. Standard error: 2.28. Cost per task in USD: 0.007438. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

LiveCodeBench v6: Easy (Show measurement, test conditions, and source)

Source value: 48.447
Score: 48.447
Metric: accuracy
Unit: %
Benchmark version: 1
Category: coding
Task: easy
Task label: Easy
Direction: Higher is better
Comparison cohort: vals-lcb:1:easy
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 3.194. Standard error: 2.785. Cost per task in USD: 0.002233. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

LiveCodeBench v6: Hard (Show measurement, test conditions, and source)

Source value: 0
Score: 0
Metric: accuracy
Unit: %
Benchmark version: 1
Category: coding
Task: hard
Task label: Hard
Direction: Higher is better
Comparison cohort: vals-lcb:1:hard
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 6.583. Standard error: 0. Cost per task in USD: 0.004306. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

LiveCodeBench v6: Medium (Show measurement, test conditions, and source)

Source value: 6.266
Score: 6.266
Metric: accuracy
Unit: %
Benchmark version: 1
Category: coding
Task: medium
Task label: Medium
Direction: Higher is better
Comparison cohort: vals-lcb:1:medium
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 6.612. Standard error: 1.238. Cost per task in USD: 0.003262. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

Recorded measurement details

LegalBench: Conclusion Tasks (Show measurement, test conditions, and source)

Source value: 48.306
Score: 48.306
Metric: accuracy
Unit: %
Benchmark version: 1
Category: legal
Task: conclusion_tasks
Task label: Conclusion Tasks
Direction: Higher is better
Comparison cohort: vals-legal-bench:1:conclusion_tasks
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-10-13. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 0.238. Standard error: 1.196. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

LegalBench: Interpretation Tasks (Show measurement, test conditions, and source)

Source value: 71.624
Score: 71.624
Metric: accuracy
Unit: %
Benchmark version: 1
Category: legal
Task: interpretation_tasks
Task label: Interpretation Tasks
Direction: Higher is better
Comparison cohort: vals-legal-bench:1:interpretation_tasks
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-10-13. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 0.239. Standard error: 0.237. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

LegalBench: Issue Tasks (Show measurement, test conditions, and source)

Source value: 76.319
Score: 76.319
Metric: accuracy
Unit: %
Benchmark version: 1
Category: legal
Task: issue_tasks
Task label: Issue Tasks
Direction: Higher is better
Comparison cohort: vals-legal-bench:1:issue_tasks
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-10-13. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 0.318. Standard error: 0.708. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

LegalBench: Overall (Show measurement, test conditions, and source)

Source value: 68.287
Score: 68.287
Metric: accuracy
Unit: %
Benchmark version: 1
Category: legal
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: vals-legal-bench:1:overall
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-10-13. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 0.396. Standard error: 0.65. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

LegalBench: Rhetoric Tasks (Show measurement, test conditions, and source)

Source value: 75.635
Score: 75.635
Metric: accuracy
Unit: %
Benchmark version: 1
Category: legal
Task: rhetoric_tasks
Task label: Rhetoric Tasks
Direction: Higher is better
Comparison cohort: vals-legal-bench:1:rhetoric_tasks
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-10-13. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 0.247. Standard error: 0.842. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

LegalBench: Rule Tasks (Show measurement, test conditions, and source)

Source value: 69.553
Score: 69.553
Metric: accuracy
Unit: %
Benchmark version: 1
Category: legal
Task: rule_tasks
Task label: Rule Tasks
Direction: Higher is better
Comparison cohort: vals-legal-bench:1:rule_tasks
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: cohere/command-r-plus. Source model name: Command R+. Provider reported by source: Cohere. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-10-13. Temperature: 0.3. Maximum output tokens: 4000. Latency in seconds: 0.94. Standard error: 2.805. 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
Comparison methodology: Temperature: 0.3. Maximum output tokens: 4000

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Predecessor and successor

Sources and data date

Type	Evidence and data date
Input	Documented modalities. Retrieved September 8, 2026. Source: Model documentation · Input (Model documentation)
Output	Documented modalities. Retrieved September 8, 2026. Source: Model documentation · Input (Model documentation)
Research status	Research date September 8, 2026. 7 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Not found in the inspected sources	knowledge-cutoff Original wording The checked original card, launch post and model overview do not publish an exact cutoff for command-r-plus-04-2024.
Not found in the inspected sources	training-tokens Original wording The checked original sources do not publish a training-token count.
Unresolved	refresh-model-exclusion Original wording Earlier evidence used the same canonical ID for the August 2024 refresh. The refresh is a separate command-r-plus-08-2024 model and its facts and benchmarks are intentionally excluded from this original record.
Unresolved	AA benchmark rows Original wording No exact Artificial Analysis model page located in the checked public sitemap.
Additional source	Model metadata (retrieved September 8, 2026) · Cohere Command R and R+ model card · Editorial description reviewed October 2, 2026
Additional source	Provider model lifecycle (retrieved October 2, 2026) · Cohere model overview and lifecycle · Editorial description reviewed October 2, 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	Model metadata (retrieved September 8, 2026)
Additional source	Model metadata (retrieved September 8, 2026)
Additional source	Model metadata (retrieved September 8, 2026)
Additional source	Cohere Command R+ documentation · Editorial description reviewed October 2, 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
Additional source	Cohere changelog: Command models August refresh (2026-09-19)