mistral-large-2411: benchmark scores, price and ranking
mistral-large-2411 from Mistral AI ranks #571 of 743 LLMs on the LLMs Tiger aggregate benchmark score (45.6), based on 4 benchmark results captured up to 2026-10-13.
- Organisation
- Mistral AI
- Type
- Frontier model
- Open weights
- No
- Released
- 2024-11-18 (source: epoch)
- Cheapest API price
- $2.00 in / $6.00 out per million tokens (via vertex_ai, observed 2026-10-13T20:47Z)
- Aggregate rank
- #571 of 743 (score 45.6)
Relative to other models, mistral-large-2411 is strongest on Aider Code Editing (#27 of 92) and weakest on OTIS Mock AIME (#143 of 189).
Across the 4 benchmarks where at least ten models are scored, mistral-large-2411 is in the top 10% on 0 and in the top half on 1.
Its cheapest listed input price, $2.00 per million tokens, is lower than that of 18% of the 427 models with a listed price.
mistral-large-2411 benchmark results
| Benchmark | Score | Rank | Source | Captured |
|---|---|---|---|---|
| Aider Code Editing | 65.4% | #27 of 92 | Aider Aider Code Editing leaderboard | 2026-10-13 |
| GPQA Diamond | 51.3% | #183 of 275 | Epoch AI Benchmarking Hub | 2026-10-13 |
| DTBench | 60.8% | #101 of 146 | Epoch AI Benchmarking Hub | 2026-10-13 |
| OTIS Mock AIME | 7.8% | #143 of 189 | Epoch AI Benchmarking Hub | 2026-10-13 |
About mistral-large-2411
mistral-large-2411 is a frontier model from Mistral AI. It was released on 2024-11-18, according to Epoch AI. The cheapest API listing we track charges $2.00 per million input tokens and $6.00 per million output tokens, through vertex_ai. We hold 4 benchmark results for it across 3 areas, and none is in the top tenth of its board.
Where mistral-large-2411 stands
On our aggregate score, mistral-large-2411 is #571 of 743 ranked models, in the lower half. Its score is 45.6; the scale is centred on 50, so it sits 4.4 points below the typical model. That beats 23% of the ranked field. Among the 44 ranked Mistral AI models it is #24. The next one up is ollama/mistral-nemo:12b-instruct-2407-q4_K_M at 45.6. The next one down is mistral-medium-2505 at 45.4.
mistral-large-2411, benchmark by benchmark
Aider Code Editing puts mistral-large-2411 at 65.4%, #27 of 92. That beats 71% of the models tested, but 18.8 points short of Claude 3.5-Sonnet-20241022 (84.2%). The results either side are gpt-4-1106-preview (65.4%) and openrouter/qwen/qwen-2.5-coder-32b-instruct (65.4%). It covers Aider official benchmark. The board median is 55.6%, so mistral-large-2411 is 9.8 points above it. Source: Aider Aider Code Editing leaderboard, captured 2026-10-13.
mistral-large-2411 lands below the middle on GPQA Diamond: 51.3%, #183 of 275, with 66% of the field scoring higher. GPT-6 Astra (max) leads at 95.8%; the closest result above is Llama-4-Scout-17B-16E-Instruct at 51.8%. It covers expert-level science questions. The board median is 71.7%, so mistral-large-2411 is 20.4 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
mistral-large-2411 lands below the middle on DTBench: 60.8%, #101 of 146, with 68% of the field scoring higher. Claude Opus 5.5 (max) leads at 98.9%; the closest result above is mistral-large-2407 at 61.2%. It covers dynamic tool-use benchmark. The board median is 68.8%, so mistral-large-2411 is 8.0 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
mistral-large-2411 lands below the middle on OTIS Mock AIME: 7.8%, #143 of 189, with 75% of the field scoring higher. Claude Fable 5.1 (max) leads at 100.0%; the closest result above is Llama-4-Scout-17B-16E-Instruct at 7.8%. It covers competition maths (AIME 2024-25 style). The board median is 57.8%, so mistral-large-2411 is 50.0 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
Strengths and weaknesses
Reasoning, maths and science: weak, ahead of 29% of the field on average across 2 benchmarks. Its best board here is GPQA Diamond (#183 of 275) and its weakest is OTIS Mock AIME (#143 of 189).
Price and value
The median input price across the 427 priced models is $0.500 per million tokens, so mistral-large-2411 costs more than the typical listing. Models that score within 1.5 points or better and cost less: Qwen2.5-Coder-7B-Instruct ($0.010, 49.5), DeepSeek V4.1 Flash (high) ($0.010, 48.9), Qwen2.5-Coder-3B-Instruct ($0.010, 46.5). Similar scores cost more on Command R+ (08-24) ($2.50) and Command R+ ($2.50).
Release history
The Mistral AI model released before it was ministral-8b-2410 on 2024-10-16, which scores 42.6. The next one was pixtral-large-2411 on 2024-11-18, at 48.3.
Reading these numbers
The 4 scores on this page come from 2 sources: Epoch AI Benchmarking Hub (3), Aider Aider Code Editing leaderboard (1). 1 of them come from an official leaderboard and 3 from datasets or third-party runs. With 4 results the aggregate rank can move a lot when one new score arrives.
Models ranked nearby
- #568 MiniMax-M2.7 (45.6)
- #569 ollama/mistral-nemo:12b-instruct-2407-q4_K_M (45.6)
- #570 Llama 4 Maverick (FP8) (45.6)
- #572 gemini-2.5-flash (45.6)
- #573 qwen3-14b (45.6)
- #574 Yi-1.5-6B-Chat (45.6)
More from Mistral AI
- Mistral Large 2 (2407) (#231, 49.9)
- codestral-2405 (#352, 48.4)
- pixtral-large-2411 (#360, 48.3)
- Codestral-22B-v0.1-Q4_K_M (#392, 48.0)
- codestral:22b-v0.1-q8_0 (#393, 48.0)
- Mistral Large 4 (high) (#411, 47.7)
Compare it on the live leaderboard → · How the aggregate score works