Model comparison
Codestral vs GPT-4.1 mini
GPT-4.1 mini is the stronger model overall, scoring 33.6 to 30.6 on the Noometry Index. Codestral costs 1.6× less per token, which makes it the better buy when GPT-4.1 mini's lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
Mistral AISummary
- They share 3 benchmarks with published results for both. Codestral scores higher in 1 category and GPT-4.1 mini in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Codestral leads 19.8 to 10.8.
- The biggest single-benchmark swing is Aider Polyglot: 11.1% for Codestral and 32.4% for GPT-4.1 mini.
- Codestral is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.40 / $1.60 for GPT-4.1 mini.
- GPT-4.1 mini accepts more context: 1.05M tokens versus 256K.
Side by side
| Codestral | GPT-4.1 mini | |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 30.6 | 33.6 |
| Released | 2024-05-29 | 2025-04-14 |
| Weights | Proprietary | Proprietary |
| Context window | 256K | 1.05M |
| Max output | 8K | 33K |
| Input $ / M tokens | $0.30 | $0.40 |
| Output $ / M tokens | $0.90 | $1.60 |
| Results tracked | 7 | 47 |
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Category by category
Coding GPT-4.1 mini leads
Codestral: 27.3 (#321), GPT-4.1 mini: 30.6 (#293)
| Benchmark | Codestral | GPT-4.1 mini |
|---|---|---|
| Aider Polyglot | 11.1% | 32.4% |
| BigCodeBench Instruct | 41.8% | 48.9% |
| SWE-bench Verified (bash only) | — | 23.9% |
| SciCode | — | 40.4% |
| WeirdML | — | 37.6% |
| LMArena Coding | — | 1367 |
| BigCodeBench Complete | 52.5% | — |
| CadEval | — | 16% |
| ALE-Bench | 137.78 | — |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Agentic & Tool Use Not comparable
Codestral: —, GPT-4.1 mini: 33.3 (#55)
| Benchmark | Codestral | GPT-4.1 mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 50.5% |
Reasoning Codestral leads
Codestral: 19.8 (#251), GPT-4.1 mini: 10.8 (#340)
| Benchmark | Codestral | GPT-4.1 mini |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | 48.6% |
| ARC-AGI-2 | — | 0% |
| ARC-AGI-1 | — | 3.5% |
| CritPt | — | 0% |
| Chess Puzzles | — | 7% |
| LMArena Hard Prompts | — | 1349 |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 68.8% |
| LMCA | — | 21.1% |
| Epoch Capabilities Index | — | 135.01 |
Math Not comparable
Codestral: —, GPT-4.1 mini: 24.1 (#270)
| Benchmark | Codestral | GPT-4.1 mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 6.7% |
| OTIS Mock AIME 2024-2025 | — | 44.7% |
| Omni-MATH | — | 49.1% |
| LMArena Math | — | 1343 |
| MATH Level 5 | — | 87.3% |
| FrontierMath (Feb 2025 set) | — | 4.5% |
Knowledge Not comparable
Codestral: —, GPT-4.1 mini: 34.7 (#194)
| Benchmark | Codestral | GPT-4.1 mini |
|---|---|---|
| GPQA Diamond | — | 65.8% |
| SimpleQA Verified | — | 12.7% |
| MMLU-Pro | — | 78.3% |
| GPQA (HELM) | — | 61.4% |
| LMArena Expert | — | 1338 |
Multimodal Not comparable
Codestral: —, GPT-4.1 mini: 35.8 (#82)
| Benchmark | Codestral | GPT-4.1 mini |
|---|---|---|
| LMArena Vision | — | 1181 |
Multilingual Not comparable
Codestral: —, GPT-4.1 mini: 45.7 (#166)
| Benchmark | Codestral | GPT-4.1 mini |
|---|---|---|
| LMArena Non-English | — | 1318 |
| LMArena Chinese | — | 1329 |
| LMArena French | — | 1358 |
| LMArena German | — | 1351 |
| LMArena Japanese | — | 1290 |
| LMArena Korean | — | 1298 |
| LMArena Russian | — | 1324 |
| LMArena Spanish | — | 1319 |
Instruction Following Not comparable
Codestral: —, GPT-4.1 mini: 73.7 (#118)
| Benchmark | Codestral | GPT-4.1 mini |
|---|---|---|
| IFEval | — | 90.4% |
| LMArena Instruction Following | — | 1333 |
Long Context Not comparable
Codestral: —, GPT-4.1 mini: 31.8 (#275)
| Benchmark | Codestral | GPT-4.1 mini |
|---|---|---|
| Fiction.LiveBench | — | 44.4% |
| LMArena Longer Query | — | 1344 |
Writing & Preference Not comparable
Codestral: —, GPT-4.1 mini: 48.6 (#199)
| Benchmark | Codestral | GPT-4.1 mini |
|---|---|---|
| LMArena Text | — | 1340 |
| LMArena Creative Writing | — | 1300 |
| EQ-Bench Creative Writing | — | 1147 |
| WildBench | — | 83.8% |
| LMArena Multi-Turn | — | 1354 |
Frequently asked questions
Is Codestral better than GPT-4.1 mini?
GPT-4.1 mini is the stronger model overall, scoring 33.6 to 30.6 on the Noometry Index. Codestral costs 1.6× less per token, which makes it the better buy when GPT-4.1 mini's lead doesn't matter for your workload.
Which is cheaper, Codestral or GPT-4.1 mini?
Codestral is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; GPT-4.1 mini lists at $0.40 and $1.60.
Is Codestral or GPT-4.1 mini better for coding?
GPT-4.1 mini scores higher on coding benchmarks: 30.6 versus 27.3 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 mini does, with 1.05M tokens against 256K.
How many benchmarks do Codestral and GPT-4.1 mini share?
3 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and GPT-4.1 mini has 47.