Mistral AI, open weights

# Mistral Large

> Mistral Large by Mistral AI, released February 2024. Ranked #263 of 354 with a Noometry Index of 31.9. API: $2 in / $6 out per M tokens. 131K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/mistral-large
- Last updated: 2026-10-10
- Title: Mistral Large Benchmarks, Price & Rank (October 2026)

Mistral Large by Mistral AI ranks 263rd of 354 ranked models on the Noometry Index as of October 2026, with a score of 31.9. Its strongest category is agentic & tool use, where it ranks 89th. API pricing starts at $2 per million input tokens and $6 per million output tokens, with a 131K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #263 of 354
- **Index score:** 31.9
- **Evidence:** Confirmed 51 results
- **Provider:** [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral)
- **Released:** February 26, 2024
- **Weights:** Open weights
- **Reasoning:** No
- **Context window:** 131K
- **Max output:** 16K
- **Input price:** $2 / M
- **Output price:** $6 / M
- **Blended price:** $3 / M
- **Output speed:** Not measured
- **Value:** #182 of 219
- **Knowledge cutoff:** November 2024
- **Input:** text

## Category scores

Each category score combines every public result we have in that category.

Mistral Large category scores

1.  Coding 34.3
2.  Agentic & Tool Use 28.6
3.  Reasoning 15.8
4.  Math 18.2
5.  Knowledge 30.1
6.  Multilingual 40.0
7.  Instruction Following 67.9
8.  Long Context 38.3
9.  Writing & Preference 40.7
10.  020406080

Mistral Large category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 34.3 | #240 | 5 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 28.6 | #89 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 15.8 | #310 | 7 |
| [Math](https://noometry.com/best/math) | 18.2 | #291 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 30.1 | #230 | 6 |
| [Multilingual](https://noometry.com/best/multilingual) | 40.0 | #219 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 67.9 | #191 | 3 |
| [Long Context](https://noometry.com/best/long-context) | 38.3 | #199 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 40.7 | #242 | 7 |

## Strengths and weaknesses

Categories where Mistral Large places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Mistral Large: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 28.6 | −1.8 | #89 of 154, top 58% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 67.9 | −3.4 | #191 of 305, top 63% |
| [Long Context](https://noometry.com/best/long-context) | 38.3 | −2.7 | #199 of 296, top 68% |

### Weakest categories

Mistral Large: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Math](https://noometry.com/best/math) | 18.2 | −18.4 | #291 of 327, top 89% |
| [Reasoning](https://noometry.com/best/reasoning) | 15.8 | −7.8 | #310 of 350, top 89% |
| [Writing & Preference](https://noometry.com/best/writing) | 40.7 | −13.1 | #242 of 312, top 78% |

## Closest competitors

The models ranked just above and below Mistral Large. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Mistral Large
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Pixtral Large](https://noometry.com/models/pixtral-large) | #259 | 32.2 | $3 | — | [Compare](https://noometry.com/compare/mistral-large-vs-pixtral-large) |
| [Falcon-180B](https://noometry.com/models/falcon-180b) | #260 | 32.2 | — | — | [Compare](https://noometry.com/compare/falcon-180b-vs-mistral-large) |
| [Gemini 1.5 Pro (May 2024)](https://noometry.com/models/gemini-1-5-pro) | #261 | 32.1 | — | — | [Compare](https://noometry.com/compare/gemini-1-5-pro-vs-mistral-large) |
| [Gemma 3 12B](https://noometry.com/models/gemma-3-12b) | #262 | 32.1 | $0.075 | — | [Compare](https://noometry.com/compare/gemma-3-12b-vs-mistral-large) |
| [Qwen3-4B](https://noometry.com/models/qwen3-4b) | #264 | 31.9 | — | — | [Compare](https://noometry.com/compare/mistral-large-vs-qwen3-4b) |
| [Amazon Nova Lite](https://noometry.com/models/amazon-nova-lite) | #265 | 31.9 | $0.10 | — | [Compare](https://noometry.com/compare/amazon-nova-lite-vs-mistral-large) |
| [Mistral Medium 3.1](https://noometry.com/models/mistral-medium-3-1) | #266 | 31.9 | $0.80 | — | [Compare](https://noometry.com/compare/mistral-large-vs-mistral-medium-3-1) |
| [Qwen2.5 72B Instruct](https://noometry.com/models/qwen2-5-72b-instruct) | #267 | 31.9 | $2.45 | — | [Compare](https://noometry.com/compare/mistral-large-vs-qwen2-5-72b-instruct) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

Mistral Large Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SciCode](https://noometry.com/benchmarks/scicode) | 36.2% | #91 of 121, top 76% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BigCodeBench Instruct](https://noometry.com/benchmarks/bigcodebench-instruct) | 30% | #55 of 64, top 86% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-02-26 |
| [LiveBench Coding](https://noometry.com/benchmarks/livebench-coding) | 47.1% | #20 of 39, top 52% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1275 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1183 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1277 | #211 of 294, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [BigCodeBench Complete](https://noometry.com/benchmarks/bigcodebench-complete) | 38.3% | #56 of 66, top 85% |  | [BigCodeBench](https://bigcode-bench.github.io/) | 2024-02-26 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 264.7 | #100 of 105, top 96% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [HumanEval+](https://noometry.com/benchmarks/humaneval-plus) | 62.2% | #26 of 45, top 58% | mar 2024 | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |
| [MBPP+](https://noometry.com/benchmarks/mbpp-plus) | 59.5% | #25 of 38, top 66% | mar 2024 | [EvalPlus](https://evalplus.github.io/leaderboard.html) |  |

### Agentic & Tool Use

Mistral Large Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 38.4% | #24 of 49, top 49% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |

### Reasoning

Mistral Large Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 22.5% | #71 of 77, top 93% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0% | #123 of 134, top 92% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Reasoning](https://noometry.com/benchmarks/livebench-reasoning) | 43.5% | #25 of 39, top 65% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1256 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1167 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1257 | #215 of 297, top 73% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 55.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 61.2% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 65.1% | #101 of 151, top 67% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 60.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench Data Analysis](https://noometry.com/benchmarks/livebench-data-analysis) | 50.1% | #22 of 39, top 57% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 16.7% | #101 of 125, top 81% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 122.01 |  |  | [Epoch AI](https://epoch.ai/eci) | 2024-02-26 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 127.54 |  |  | [Epoch AI](https://epoch.ai/eci) | 2024-07-24 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 128.52 | #145 of 213, top 69% |  | [Epoch AI](https://epoch.ai/eci) | 2024-11-18 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 57.1 | #62 of 72, top 87% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 56.9 |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LiveBench](https://noometry.com/benchmarks/livebench) | 48.4% | #22 of 39, top 57% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Mistral Large Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 7.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-25 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 8.5% | #135 of 173, top 79% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-25 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 1.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-25 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 28.1% | #41 of 57, top 72% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LiveBench Math](https://noometry.com/benchmarks/livebench-math) | 42.5% | #23 of 39, top 59% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1261 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1262 | #208 of 285, top 73% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1200 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 44.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 24.5% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 50.3% | #45 of 79, top 57% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-25 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 0.3% | #66 of 68, top 98% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-03-06 |

### Knowledge

Mistral Large Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 49% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 38.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-01-27 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 51.3% | #126 of 186, top 68% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-02-25 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 59.9% | #45 of 58, top 78% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 21.4% | #35 of 51, top 69% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 4.5% | #6 of 96, top 7% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 43.5% | #39 of 57, top 69% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1123 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1232 | #208 of 273, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1208 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 68.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [MMLU](https://noometry.com/benchmarks/mmlu) | 80% | #17 of 81, top 21% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Multilingual

Mistral Large Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1142 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1237 | #219 of 297, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1235 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1120 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1240 | #215 of 285, top 76% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1239 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1210 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1325 | #155 of 223, top 70% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1273 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1178 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1254 | #174 of 231, top 76% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1242 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1188 | #164 of 211, top 78% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1008 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1170 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1170 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1017 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1202 | #161 of 213, top 76% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1180 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1257 | #207 of 283, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1252 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1236 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1268 | #174 of 226, top 77% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1195 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Mistral Large Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LiveBench Instruction Following](https://noometry.com/benchmarks/livebench-if) | 67.9% | #21 of 39, top 54% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 87.7% | #14 of 57, top 25% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1249 | #213 of 298, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1248 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1169 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Mistral Large Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1261 | #214 of 291, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1257 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1173 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Mistral Large Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1266 | #218 of 297, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1177 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1265 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1243 | #212 of 295, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1161 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1242 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Short-Story Creative Writing](https://noometry.com/benchmarks/lech-mazur-writing) | 69% | #32 of 39, top 83% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 985 | #96 of 115, top 84% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 80.1% | #29 of 57, top 51% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1172 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1257 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1260 | #217 of 295, top 74% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LiveBench Language](https://noometry.com/benchmarks/livebench-language) | 39.4% | #19 of 39, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

## API pricing by provider

Mistral Large API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [mistral](https://docs.mistral.ai/getting-started/models/) | $2 | $6 | — | 2026-10-10 |
| [openrouter](https://openrouter.ai/mistralai/mistral-large) | $2 | $6 | $0.20 | 2026-10-10 |

[All Mistral AI API prices →](https://noometry.com/llm-pricing/mistral) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare Mistral Large

-   [Mistral Large vs Gemma 3 12B](https://noometry.com/compare/gemma-3-12b-vs-mistral-large)
-   [Mistral Large vs Qwen3-4B](https://noometry.com/compare/mistral-large-vs-qwen3-4b)
-   [Mistral Large vs Gemini 1.5 Pro (May 2024)](https://noometry.com/compare/gemini-1-5-pro-vs-mistral-large)
-   [Mistral Large vs Amazon Nova Lite](https://noometry.com/compare/amazon-nova-lite-vs-mistral-large)
-   [Mistral Large vs Falcon-180B](https://noometry.com/compare/falcon-180b-vs-mistral-large)
-   [Mistral Large vs Mistral Medium 3.1](https://noometry.com/compare/mistral-large-vs-mistral-medium-3-1)
-   [Mistral Large vs GPT-6 Astra](https://noometry.com/compare/gpt-6-astra-vs-mistral-large)
-   [Mistral Large vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-mistral-large)
-   [Mistral Large vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-mistral-large)
-   [Mistral Large vs Kimi K3](https://noometry.com/compare/kimi-k3-vs-mistral-large)
-   [Mistral Large vs Grok 4.6](https://noometry.com/compare/grok-4-6-vs-mistral-large)
-   [Mistral Large vs Qwen3.8 Max](https://noometry.com/compare/mistral-large-vs-qwen3-8-max)
-   [Mistral Large vs GLM-5.3](https://noometry.com/compare/glm-5-3-vs-mistral-large)
-   [Mistral Large vs Muse Spark 1.3](https://noometry.com/compare/mistral-large-vs-muse-spark-1-3)

## Other Mistral AI models

-   [Mistral Large 4](https://noometry.com/models/mistral-large-4)43.1
-   [Mistral Medium 3.5](https://noometry.com/models/mistral-medium-3-5)40.2
-   [Mistral Large 3](https://noometry.com/models/mistral-large-3)39.1
-   [Mistral Medium](https://noometry.com/models/mistral-medium)36.3
-   [Magistral Medium](https://noometry.com/models/magistral-medium)35.2
-   [Devstral Small 2505](https://noometry.com/models/devstral-small)34.3
-   [Mistral Small](https://noometry.com/models/mistral-small)33.4
-   [Pixtral Large](https://noometry.com/models/pixtral-large)32.2

## Frequently asked questions

### How good is Mistral Large?

Mistral Large by Mistral AI ranks 263rd of 354 ranked models on the Noometry Index as of October 2026, with a score of 31.9. Its strongest category is agentic & tool use, where it ranks 89th. API pricing starts at $2 per million input tokens and $6 per million output tokens, with a 131K-token context window.

### How much does Mistral Large cost?

Mistral Large costs $2 per million input tokens and $6 per million output tokens on Mistral AI's own API.

### What is Mistral Large's context window?

Mistral Large accepts up to 131K tokens of input and can write up to 16K tokens in one response.

### Is Mistral Large open source?

Yes. Mistral Large's weights are downloadable; check the license for commercial terms.

### What are Mistral Large's strengths and weaknesses?

Relative to other ranked models, Mistral Large places best in agentic & tool use, instruction following, long context and lowest in math, reasoning, writing & preference.

### What is Mistral Large best at?

Its best category is agentic & tool use, where it ranks 89th on Noometry.

### Cite this page

Noometry. (2026). Mistral Large benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/mistral-large

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/mistral-large.md).