Model comparison
Claude Haiku 4.5 vs Mistral Large 3
Claude Haiku 4.5 and Mistral Large 3 score almost the same on the Noometry Index (39.5 vs 39.1), so choose on price, context window or the category you care about most.
Last verified . 20 shared benchmarks.
Mistral AISummary
- They share 20 benchmarks with published results for both. Claude Haiku 4.5 scores higher in 4 categories and Mistral Large 3 in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in multimodal, where Mistral Large 3 leads 38.2 to 26.8.
- The biggest single-benchmark swing is NYT Connections (extended): 14.3% for Claude Haiku 4.5 and 7.5% for Mistral Large 3.
- Mistral Large 3 is cheaper at $0.25 / $0.75 per million input/output tokens, against $1 / $5 for Claude Haiku 4.5.
- Mistral Large 3 accepts more context: 262K tokens versus 200K.
- Mistral Large 3 has downloadable open weights; the other is API-only.
Side by side
| Claude Haiku 4.5 | Mistral Large 3 | |
|---|---|---|
| Provider | Anthropic | Mistral AI |
| Noometry Index | 39.5 | 39.1 |
| Released | 2025-10-15 | 2025-12-02 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 64K | 8K |
| Input $ / M tokens | $1 | $0.25 |
| Output $ / M tokens | $5 | $0.75 |
| Results tracked | 53 | 24 |
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Category by category
Coding Claude Haiku 4.5 leads
Claude Haiku 4.5: 44.0 (#78), Mistral Large 3: 34.4 (#237)
| Benchmark | Claude Haiku 4.5 | Mistral Large 3 |
|---|---|---|
| LMArena WebDev | 1330 | 1230 |
| LMArena Coding | 1453 | 1448 |
| SWE-bench Verified (bash only) | 66.6% | — |
| SWE-bench Multilingual | 64.7% | — |
| SciCode | 43.3% | — |
| WeirdML | 45.4% | — |
| ALE-Bench | 653.48 | — |
Agentic & Tool Use Not comparable
Claude Haiku 4.5: 33.6 (#52), Mistral Large 3: —
| Benchmark | Claude Haiku 4.5 | Mistral Large 3 |
|---|---|---|
| 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 Too close to call
Claude Haiku 4.5: 15.1 (#320), Mistral Large 3: 15.2 (#319)
| Benchmark | Claude Haiku 4.5 | Mistral Large 3 |
|---|---|---|
| NYT Connections (extended) | 14.3% | 7.5% |
| LMArena Hard Prompts | 1420 | 1429 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | — | 50.9% |
| ARC-AGI-1 | 47.7% | — |
| CritPt | 0% | — |
| Chess Puzzles | 8% | — |
| Thematic Generalization | — | 23% |
| DTBench | 73.6% | — |
| LMCA | 30.9% | — |
| Epoch Capabilities Index | 142.41 | — |
| ForecastBench | 61.4 | — |
Math Claude Haiku 4.5 leads
Claude Haiku 4.5: 44.9 (#78), Mistral Large 3: 38.7 (#129)
| Benchmark | Claude Haiku 4.5 | Mistral Large 3 |
|---|---|---|
| LMArena Math | 1396 | 1414 |
| OTIS Mock AIME 2024-2025 | 66.7% | — |
| Omni-MATH | 56.1% | — |
| MATH Level 5 | 96.4% | — |
| FrontierMath (Feb 2025 set) | 5.9% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Claude Haiku 4.5 leads
Claude Haiku 4.5: 37.7 (#153), Mistral Large 3: 36.0 (#177)
| Benchmark | Claude Haiku 4.5 | Mistral Large 3 |
|---|---|---|
| Vectara Hallucination Rate | 9.8% | 14.5% |
| LMArena Expert | 1442 | 1421 |
| GPQA Diamond | 71.2% | — |
| SimpleQA Verified | 13.2% | — |
| MMLU-Pro | 77.7% | — |
| GPQA (HELM) | 60.5% | — |
Multimodal Mistral Large 3 leads
Claude Haiku 4.5: 26.8 (#118), Mistral Large 3: 38.2 (#66)
| Benchmark | Claude Haiku 4.5 | Mistral Large 3 |
|---|---|---|
| LMArena Vision | — | 1221 |
| Blueprint-Bench 2 | 0% | — |
| LMArena Document | 1420 | — |
Multilingual Mistral Large 3 leads
Claude Haiku 4.5: 49.9 (#129), Mistral Large 3: 52.5 (#84)
| Benchmark | Claude Haiku 4.5 | Mistral Large 3 |
|---|---|---|
| LMArena Non-English | 1377 | 1413 |
| LMArena Chinese | 1417 | 1447 |
| LMArena French | 1408 | 1455 |
| LMArena German | 1375 | 1437 |
| LMArena Japanese | 1339 | 1394 |
| LMArena Korean | 1347 | 1384 |
| LMArena Russian | 1381 | 1411 |
| LMArena Spanish | 1420 | 1440 |
Instruction Following Mistral Large 3 leads
Claude Haiku 4.5: 71.4 (#149), Mistral Large 3: 74.0 (#108)
| Benchmark | Claude Haiku 4.5 | Mistral Large 3 |
|---|---|---|
| LMArena Instruction Following | 1414 | 1403 |
| IFEval | 80.1% | — |
Long Context Too close to call
Claude Haiku 4.5: 43.6 (#92), Mistral Large 3: 43.1 (#105)
| Benchmark | Claude Haiku 4.5 | Mistral Large 3 |
|---|---|---|
| LMArena Longer Query | 1427 | 1413 |
Writing & Preference Mistral Large 3 leads
Claude Haiku 4.5: 57.9 (#123), Mistral Large 3: 60.0 (#101)
| Benchmark | Claude Haiku 4.5 | Mistral Large 3 |
|---|---|---|
| LMArena Text | 1396 | 1428 |
| LMArena Creative Writing | 1372 | 1386 |
| LMArena Multi-Turn | 1409 | 1429 |
| EQ-Bench Creative Writing | — | 1412 |
| WildBench | 83.9% | — |
| EQ-Bench 4 | 1064 | — |
Frequently asked questions
Is Claude Haiku 4.5 better than Mistral Large 3?
Claude Haiku 4.5 and Mistral Large 3 score almost the same on the Noometry Index (39.5 vs 39.1), so choose on price, context window or the category you care about most.
Which is cheaper, Claude Haiku 4.5 or Mistral Large 3?
Mistral Large 3 is cheaper. It lists at $0.25 per million input tokens and $0.75 per million output tokens; Claude Haiku 4.5 lists at $1 and $5.
Is Claude Haiku 4.5 or Mistral Large 3 better for coding?
Claude Haiku 4.5 scores higher on coding benchmarks: 44.0 versus 34.4 in the Noometry coding category.
Which has the bigger context window?
Mistral Large 3 does, with 262K tokens against 200K.
How many benchmarks do Claude Haiku 4.5 and Mistral Large 3 share?
20 benchmarks have published results for both models. Claude Haiku 4.5 has 53 scored results on Noometry and Mistral Large 3 has 24.