Language modelOpen source

Kimi K2 0711

Moonshot AI

Released: July 11, 2025
Data date: September 8, 2026
Successor: Kimi K2 0905

Benchmark-Meta-Score: Unavailable
Results from one source only
Speed: 40.1 tokens/s
Output throughput
Price: $0.57 · $2.3
Input · Output / 1M tokens
Input: Text
Output: Text

Kimi K2 0711 is the dated Instruct release from July 11, 2025. Moonshot’s K2 mixture-of-experts design has 1 trillion total parameters and 32 billion active parameters; this checkpoint uses a 131,072-token context window.

The later release Kimi K2 0905 expands the context to 262,144 tokens. Kimi discontinued the corresponding kimi-k2-0711-preview API entry on May 25, 2026.

Specifications and access

Specification	Value and source
Weights	Open weightsSource
Access	API, LocalSource
Parameters	1TSource
Active parameters	32BSource
Context window	131,072 tokensSource
License	modified MITSource
Parameters	1,000 BSource
Active parameters	32 BSource
Architecture	MoE with MLA and MoonViTSource
Input	TextSource
Output	TextSource
Reasoning mode	YesSource
Tool calling	YesSource
API model ID	kimi-k2-0711 (The 0711 release is represented by this card; 0905 card compares against it)Source
Availability	Corresponding first-party API ID `kimi-k2-0711-preview` retired 2026-05-25 as part of the K2 series. (Released weights and third-party provider availability are separate from the first-party API lifecycle.)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 results from at least two sources.

provisional

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

Test series: 10
Test families: 10 / 65
Sources: 1
Share of the fixed test panel (informative): 17 %

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 0.2
Test series: 2
Test families: 2 / 15
Sources: 1
Share of the fixed test panel (informative): 8 %

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 0.1
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: ± 0.1
95% interval: 0.0 to 0.6
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 0.1
Test series: 1
Test families: 1 / 3
Sources: 1
Share of the fixed test panel (informative): 33 %

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 0.3
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: ± 0.1
95% interval: 0.0 to 0.8
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 10 test families and 10 of 10 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: 19.7 / 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:
  71.464 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  77
  Rank percentile within the cohort:
  19.7 %
  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: 0.6. Maximum output tokens: 32768
  Source model ID: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 41.434. Standard error: 2.266. Cost per task in USD: 0.004481. 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: Terminal-Bench
Coding · Mean rank percentile of the family: 19.0 / 100
Family 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:
  25.843 %
  Metric:
  accuracy
  Version:
  2.0
  Participating models:
  43
  Rank percentile within the cohort:
  19.0 %
  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: 16384
  Source model ID: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-06-04. Temperature: 0.3. Maximum output tokens: 16384. Latency in seconds: 702.2. Standard error: 4.667. Cost per task in USD: 1.107135. 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
Test family: CorpFin v2
Finance · Mean rank percentile of the family: 10.8 / 100
Family 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:
  50.388 %
  Metric:
  accuracy
  Version:
  2
  Participating models:
  75
  Rank percentile within the cohort:
  10.8 %
  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: 16384
  Source model ID: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Temperature: 0.3. Maximum output tokens: 16384. Latency in seconds: 40.894. Standard error: 0.985. Cost per task in USD: 0.059336. 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
Test family: LiveCodeBench v6
Coding · Mean rank percentile of the family: 24.7 / 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:
  70.449 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  78
  Rank percentile within the cohort:
  24.7 %
  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: 0.6. Maximum output tokens: 32768
  Source model ID: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 66.651. Standard error: 1.159. Cost per task in USD: 0.006889. 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: 38.5 / 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:
  81.454 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  79
  Rank percentile within the cohort:
  38.5 %
  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: 0.6. Maximum output tokens: 32768
  Source model ID: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-10-13. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 1.172. Standard error: 0.444. 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: MATH 500
Mathematics · Mean rank percentile of the family: 77.3 / 100
Family 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:
  94.2 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  23
  Rank percentile within the cohort:
  77.3 %
  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: 0.6. Maximum output tokens: 32768
  Source model ID: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-01-09. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 37.346. Standard error: 1.045. Cost per task in USD: 0.002985. 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
Test family: MedQA
Health · Mean rank percentile of the family: 30.8 / 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:
  83.975 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  40
  Rank percentile within the cohort:
  30.8 %
  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: 0.6. Maximum output tokens: 32768
  Source model ID: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 10.984. Standard error: 0.335. Cost per task in USD: 0.001028. 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: MGSM
Mathematics · Mean rank percentile of the family: 62.1 / 100
Family 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:
  90.946 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  30
  Rank percentile within the cohort:
  62.1 %
  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: 0.6. Maximum output tokens: 32768
  Source model ID: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-01-09. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 11.83. Standard error: 1.766. Cost per task in USD: 0.001001. 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
Test family: MMLU-Pro
Knowledge · Mean rank percentile of the family: 19.5 / 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:
  79.394 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  78
  Rank percentile within the cohort:
  19.5 %
  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: 0.6. Maximum output tokens: 32768
  Source model ID: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 20.338. Standard error: 0.394. Cost per task in USD: 0.002836. 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
Test family: TaxEval v2
Finance · Mean rank percentile of the family: 23.7 / 100
Family 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:
  70.196 %
  Metric:
  accuracy
  Version:
  2
  Participating models:
  77
  Rank percentile within the cohort:
  23.7 %
  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: 0.6. Maximum output tokens: 32768
  Source model ID: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-01. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 12.834. Standard error: 0.895. Cost per task in USD: 0.001543. 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

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	1	1	8
Vals AI	11	0	62

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.

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.

AIME: AIME 2024 (Show measurement, test conditions, and source)

Source value: 70
Score: 70
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Temperature: 0.6. Latency in seconds: 110.392. Standard error: 1.543. Cost per task in USD: 0.011401. 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: 0.6
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: 55.417
Score: 55.417
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Temperature: 0.6. Latency in seconds: 138.944. Standard error: 1.883. Cost per task in USD: 0.01416. 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: 0.6
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: 62.708
Score: 62.708
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Temperature: 0.6. Latency in seconds: 124.668. Standard error: 1.217. Cost per task in USD: 0.01278. 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: 0.6
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: 49.417
Score: 49.417
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Temperature: 0.3. Maximum output tokens: 16384. Latency in seconds: 46.214. Standard error: 1.707. Cost per task in USD: 0.111403. 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: 16384

CorpFin v2: Max Fitting Context (Show measurement, test conditions, and source)

Source value: 53.03
Score: 53.03
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Temperature: 0.3. Maximum output tokens: 16384. Latency in seconds: 33.801. Standard error: 1.704. Cost per task in USD: 0.001714. 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: 16384

CorpFin v2: Overall (Show measurement, test conditions, and source)

Source value: 50.388
Score: 50.388
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Temperature: 0.3. Maximum output tokens: 16384. Latency in seconds: 40.894. Standard error: 0.985. Cost per task in USD: 0.059336. 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: 16384

CorpFin v2: Shared Max Context (Show measurement, test conditions, and source)

Source value: 48.718
Score: 48.718
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Temperature: 0.3. Maximum output tokens: 16384. Latency in seconds: 42.666. Standard error: 1.706. Cost per task in USD: 0.064891. 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: 16384

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

Source value: 69.192
Score: 69.192
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 30.413. Standard error: 3.281. Cost per task in USD: 0.004435. 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: 0.6. Maximum output tokens: 32768

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

Source value: 73.737
Score: 73.737
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 52.454. Standard error: 3.127. Cost per task in USD: 0.004527. 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: 0.6. Maximum output tokens: 32768

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

Source value: 71.464
Score: 71.464
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 41.434. Standard error: 2.266. Cost per task in USD: 0.004481. 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: 0.6. Maximum output tokens: 32768

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

Source value: 97.205
Score: 97.205
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 17.242. Standard error: 0.919. Cost per task in USD: 0.002214. 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: 0.6. Maximum output tokens: 32768

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

Source value: 36.857
Score: 36.857
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: together/moonshotai/Kimi-K2-Instruct. Source model name: Kimi K2 0711. Provider reported by source: Together AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Temperature: 0.6. Maximum output tokens: 32768. Latency in seconds: 126.698. Standard error: 2.579. Cost per task in USD: 0.012321. 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: 0.6. Maximum output tokens: 32768

Page 1 of 7

Predecessor and successor

Sources and data date

Type	Evidence and data date
Speed	Median across serving providers. Output speed after the first streaming chunk, not total response time. Classified as deprecated by Artificial Analysis. Artificial Analysis maintains only the default 10K-input workload there. Retrieved September 8, 2026. Source: Artificial Analysis · Artificial Analysis (retrieved September 8, 2026)Speed measurement methodology
Price	Base API prices without caching or batch discounts. Higher context tiers and other rates are listed under Costs. Source: Pricing · API pricing
Input	Documented modalities. Retrieved September 8, 2026. Source: Model documentation · Model metadata (retrieved September 8, 2026) · Kimi K2 Instruct model card · Editorial description reviewed October 2, 2026
Output	Documented modalities. Retrieved September 8, 2026. Source: Model documentation · Model metadata (retrieved September 8, 2026) · Kimi K2 Instruct model card · Editorial description reviewed October 2, 2026
Research status	Research date September 8, 2026. 4 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Unresolved	release-date Original wording No normalized release-date field in checked cards.
Unresolved	benchmark-provenance Original wording The exact K2-0711 column is published in the later 0905 card rather than a dedicated 0711 card table.
Additional source	Model metadata
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	Kimi K2 Instruct-0905 model card (retrieved September 8, 2026) · Kimi K2 0905 model card · Editorial description reviewed October 2, 2026
Additional source	Model metadata (retrieved September 8, 2026) · Kimi API model list · 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	Moonshot AI, Kimi-K2-Instruct-0905 model card (Hugging Face) (2026-09-19)