Model comparison
Claude Opus 4.5 vs Mistral Large 3
Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Opus 4.5 #34 (Supported); Mistral Large 3 #113 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.5 and Mistral Large 3 share 12 comparable benchmark results. 0 of 8 categories are comparable. 47 results are unique to Claude Opus 4.5; 4 to Mistral Large 3.
Updated July 22, 2026- Shared results
- 12
- Claude Opus 4.5 only
- 47
- Mistral Large 3 only
- 4
- Comparable categories
- 0 / 8
Benchmark data for Claude Opus 4.5 and Mistral Large 3 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Claude Opus 4.5 is priced at $5.00 input / $25.00 output per 1M tokens, versus $0.50 input / $1.50 output per 1M tokens for Mistral Large 3. Claude Opus 4.5 has the larger context window at 200K, compared with 128K for Mistral Large 3.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Claude Opus 4.5 | Δ | Mistral Large 3 |
|---|---|---|---|
| Agentic | Claude Opus 4.562.6 | MarginNo overlap | Mistral Large 3Not measured |
| Coding | Claude Opus 4.571.7 | MarginNo overlap | Mistral Large 3Not measured |
| Reasoning | Claude Opus 4.564.4 | MarginNo overlap | Mistral Large 3Not measured |
| Knowledge | Claude Opus 4.558.1 | MarginNo overlap | Mistral Large 3Not measured |
| Math | Claude Opus 4.557.5 | MarginNo overlap | Mistral Large 3Not measured |
| Multilingual | Claude Opus 4.585.7 | MarginNo overlap | Mistral Large 3Not measured |
| Multimodal | Claude Opus 4.569.9 | MarginNo overlap | Mistral Large 3Not measured |
| Inst. Following | Claude Opus 4.569.5 | MarginNo overlap | Mistral Large 3Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Opus 4.5 | Mistral Large 3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.5$5 input / $25 output | Mistral Large 3$0.5 input / $1.5 output | Mistral Large 3 has the lower combined listed price. |
| Generation speedtokens per second | Claude Opus 4.546 tok/s | Mistral Large 348 tok/s | Mistral Large 3 has the higher measured throughput. |
| First-answer latencyseconds to first token | Claude Opus 4.51.01 s | Mistral Large 31.04 s | Claude Opus 4.5 reaches the first token sooner. |
| Context windowmaximum listed tokens | Claude Opus 4.5200K | Mistral Large 3128K | Claude Opus 4.5 lists the larger context window. |
Benchmark Deep Dive
Agentic19 benchmarks
| Benchmark | Claude Opus 4.5 | Mistral Large 3 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 59.3% | — | Not comparable |
| OSWorld-VerifiedSource | 66.3% | — | Not comparable |
| OSWorldSource | 66.3% | — | Not comparable |
| Claw-EvalSource | 59.6% | — | Not comparable |
| QwenClawBenchSource | 52.3% | — | Not comparable |
| τ³-bench resultsSource | 70.2% | — | Not comparable |
| VITA-BenchSource | 23.3% | — | Not comparable |
| DeepPlanningSource | 26.4% | — | Not comparable |
| ToolathlonSource | 43.5% | — | Not comparable |
| MCP AtlasSource | 42.3% | — | Not comparable |
| MCP-TasksSource | 71.8% | — | Not comparable |
| WideResearchSource | 76.4% | — | Not comparable |
| CyberGymSource | 50.6% | — | Not comparable |
| τ²-bench resultsSource | 86.3% | 24.6% | Claude Opus 4.5 leads |
| Gert LabsSource | 64.23% | — | Not comparable |
| JobBenchSource | 32.3% | — | Not comparable |
| AA Agentic IndexSource | — | 5.5% | Not comparable |
| GDPval-AASource | — | 6.6% | Not comparable |
| GDPval-AASource | — | 633 | Not comparable |
Coding7 benchmarks
| Benchmark | Claude Opus 4.5 | Mistral Large 3 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 80.9% | — | Not comparable |
| LiveCodeBench v6Source | 84.8% | — | Not comparable |
| SWE-bench ProSource | 57.1% | — | Not comparable |
| SWE MultilingualSource | 77.5% | — | Not comparable |
| NL2RepoSource | 43.2% | — | Not comparable |
| AA-SciCodeSource | 47.0% | 36.2% | Claude Opus 4.5 leads |
| AA Coding IndexSource | — | 20.1% | Not comparable |
Reasoning4 benchmarks
Knowledge13 benchmarks
| Benchmark | Claude Opus 4.5 | Mistral Large 3 | Result |
|---|---|---|---|
| GPQASource | 87% | — | Not comparable |
| SuperGPQASource | 70.6% | — | Not comparable |
| MMLU-ProSource | 89.5% | — | Not comparable |
| MMLU-ReduxSource | 96.6% | — | Not comparable |
| C-EvalSource | 92.2% | — | Not comparable |
| HLESource | 30.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 34.7% | 15.9% | Claude Opus 4.5 leads |
| AA-GPQA DiamondSource | 81.0% | 68.0% | Claude Opus 4.5 leads |
| AA-HLESource | 12.9% | 4.1% | Claude Opus 4.5 leads |
| AA-Omniscience IndexSource | -3.9% | -39.4% | Claude Opus 4.5 leads |
| AA-Omniscience AccuracySource | 40.7% | 24.1% | Claude Opus 4.5 leads |
| AA-Omniscience Hallucination RateSource | 75.4% | 83.7% | Claude Opus 4.5 leads |
| AA MMLU-ProSource | 88.9% | — | Not comparable |
Math7 benchmarks
| Benchmark | Claude Opus 4.5 | Mistral Large 3 | Result |
|---|---|---|---|
| AIME26Source | 95.1% | — | Not comparable |
| HMMT Feb 2025Source | 92.9% | — | Not comparable |
| HMMT Nov 2025Source | 93.3% | — | Not comparable |
| HMMT Feb 2026Source | 85.3% | — | Not comparable |
| MMAnswerBenchSource | 84.0% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 20.690% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 4.167% | — | Not comparable |
Multilingual2 benchmarks
Multimodal8 benchmarks
| Benchmark | Claude Opus 4.5 | Mistral Large 3 | Result |
|---|---|---|---|
| MMMU-ProSource | 70.6% | — | Not comparable |
| MathVisionSource | 74.3% | — | Not comparable |
| CharXivSource | 68.5% | — | Not comparable |
| VideoMMMUSource | 84.4% | — | Not comparable |
| ScreenSpot ProSource | 45.7% | — | Not comparable |
| V*Source | 67.0% | — | Not comparable |
| AA-MMMU-ProSource | 71.2% | 55.7% | Claude Opus 4.5 leads |
| Design Arena WebsiteSource | 1277 | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Claude Opus 4.5 and Mistral Large 3 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Claude Opus 4.5 and Mistral Large 3 today?
Claude Opus 4.5: $5.00 input / $25.00 output per 1M tokens Mistral Large 3: $0.50 input / $1.50 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Related Comparisons
Choose a model with this week’s evidence
Join 2,000+ readers for ranking moves, pricing changes, and the claims that still need proof.
One email each week. Unsubscribe anytime.