Model comparison
Claude Haiku 4.5 vs Codestral
Claude Haiku 4.5 is the stronger model overall, scoring 39.3 to 30.1 on the Noometry Index. Codestral costs 4.4× less per token, which makes it the better buy when Claude Haiku 4.5's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
Summary
- They share 1 benchmark with published results for both. Claude Haiku 4.5 scores higher in 1 category and Codestral in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where Claude Haiku 4.5 leads 43.9 to 26.9.
- Codestral is cheaper at $0.30 / $0.90 per million input/output tokens, against $1 / $5 for Claude Haiku 4.5.
- Codestral accepts more context: 256K tokens versus 200K.
- Codestral has downloadable open weights; the other is API-only.
Side by side
| Claude Haiku 4.5 | Codestral | |
|---|---|---|
| Provider | Anthropic | Mistral AI |
| Noometry Index | 39.3 | 30.1 |
| Released | 2025-10-15 | 2024-05-29 |
| Weights | Proprietary | Open |
| Context window | 200K | 256K |
| Max output | 64K | 4K |
| Input $ / M tokens | $1 | $0.30 |
| Output $ / M tokens | $5 | $0.90 |
| Results tracked | 53 | 7 |
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Category by category
Coding Claude Haiku 4.5 leads
Claude Haiku 4.5: 43.9 (#71), Codestral: 26.9 (#316)
| Benchmark | Claude Haiku 4.5 | Codestral |
|---|---|---|
| ALE-Bench | 653.48 | 137.78 |
| SWE-bench Verified (bash only) | 66.6% | — |
| Aider Polyglot | — | 11.1% |
| LMArena WebDev | 1330 | — |
| SWE-bench Multilingual | 64.7% | — |
| SciCode | 43.3% | — |
| WeirdML | 45.4% | — |
| BigCodeBench Instruct | — | 41.8% |
| LMArena Coding | 1454 | — |
| BigCodeBench Complete | — | 52.5% |
| HumanEval+ | — | 73.8% |
| MBPP+ | — | 61.9% |
Agentic & Tool Use Not comparable
Claude Haiku 4.5: 33.5 (#50), Codestral: —
| Benchmark | Claude Haiku 4.5 | Codestral |
|---|---|---|
| Terminal-Bench | 35.5% | — |
| Berkeley Function Calling Leaderboard | 68.7% | — |
| DeepResearch Bench | 45.5% | — |
| BALROG | 31.2% | — |
| ExploitBench | 13.7% | — |
| Vending-Bench 2 | 458.89 | — |
Reasoning Codestral leads
Claude Haiku 4.5: 15.1 (#310), Codestral: 19.1 (#244)
| Benchmark | Claude Haiku 4.5 | Codestral |
|---|---|---|
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | — | 32.5% |
| NYT Connections (extended) | 14.3% | — |
| ARC-AGI-1 | 47.7% | — |
| CritPt | 0% | — |
| Chess Puzzles | 8% | — |
| LMArena Hard Prompts | 1420 | — |
| DTBench | 73.6% | — |
| LMCA | 30.9% | — |
| Epoch Capabilities Index | 142.42 | — |
| ForecastBench | 61.4 | — |
Math Not comparable
Claude Haiku 4.5: 43.0 (#69), Codestral: —
| Benchmark | Claude Haiku 4.5 | Codestral |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.7% | — |
| Omni-MATH | 56.1% | — |
| LMArena Math | 1395 | — |
| MATH Level 5 | 96.4% | — |
| FrontierMath (Feb 2025 set) | 5.9% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Not comparable
Claude Haiku 4.5: 37.6 (#146), Codestral: —
| Benchmark | Claude Haiku 4.5 | Codestral |
|---|---|---|
| GPQA Diamond | 71.2% | — |
| SimpleQA Verified | 13.2% | — |
| MMLU-Pro | 77.7% | — |
| Vectara Hallucination Rate | 9.8% | — |
| GPQA (HELM) | 60.5% | — |
| LMArena Expert | 1443 | — |
Multimodal Not comparable
Claude Haiku 4.5: 27.0 (#111), Codestral: —
| Benchmark | Claude Haiku 4.5 | Codestral |
|---|---|---|
| Blueprint-Bench 2 | 0% | — |
| LMArena Document | 1420 | — |
Multilingual Not comparable
Claude Haiku 4.5: 50.1 (#124), Codestral: —
| Benchmark | Claude Haiku 4.5 | Codestral |
|---|---|---|
| LMArena Non-English | 1376 | — |
| LMArena Chinese | 1415 | — |
| LMArena French | 1408 | — |
| LMArena German | 1377 | — |
| LMArena Japanese | 1339 | — |
| LMArena Korean | 1346 | — |
| LMArena Russian | 1380 | — |
| LMArena Spanish | 1420 | — |
Instruction Following Not comparable
Claude Haiku 4.5: 71.6 (#144), Codestral: —
| Benchmark | Claude Haiku 4.5 | Codestral |
|---|---|---|
| IFEval | 80.1% | — |
| LMArena Instruction Following | 1414 | — |
Long Context Not comparable
Claude Haiku 4.5: 43.6 (#86), Codestral: —
| Benchmark | Claude Haiku 4.5 | Codestral |
|---|---|---|
| LMArena Longer Query | 1427 | — |
Writing & Preference Not comparable
Claude Haiku 4.5: 58.1 (#119), Codestral: —
| Benchmark | Claude Haiku 4.5 | Codestral |
|---|---|---|
| LMArena Text | 1396 | — |
| LMArena Creative Writing | 1371 | — |
| WildBench | 83.9% | — |
| EQ-Bench 4 | 1064 | — |
| LMArena Multi-Turn | 1409 | — |
Frequently asked questions
Is Claude Haiku 4.5 better than Codestral?
Claude Haiku 4.5 is the stronger model overall, scoring 39.3 to 30.1 on the Noometry Index. Codestral costs 4.4× less per token, which makes it the better buy when Claude Haiku 4.5's lead doesn't matter for your workload.
Which is cheaper, Claude Haiku 4.5 or Codestral?
Codestral is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Claude Haiku 4.5 lists at $1 and $5.
Is Claude Haiku 4.5 or Codestral better for coding?
Claude Haiku 4.5 scores higher on coding benchmarks: 43.9 versus 26.9 in the Noometry coding category.
Which has the bigger context window?
Codestral does, with 256K tokens against 200K.
How many benchmarks do Claude Haiku 4.5 and Codestral share?
1 benchmark has published results for both models. Claude Haiku 4.5 has 53 scored results on Noometry and Codestral has 7.