Model comparison
MAI-Thinking-1 vs Mistral Large 3
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: MAI-Thinking-1 unranked (Not scored); Mistral Large 3 #113 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. MAI-Thinking-1 and Mistral Large 3 share 0 comparable benchmark results. 0 of 8 categories are comparable. 13 results are unique to MAI-Thinking-1; 16 to Mistral Large 3.
Updated July 23, 2026- Shared results
- 0
- MAI-Thinking-1 only
- 13
- Mistral Large 3 only
- 16
- Comparable categories
- 0 / 8
Benchmark data for MAI-Thinking-1 and Mistral Large 3 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 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.
MAI-Thinking-1 has the larger context window at 256K, compared with 128K for Mistral Large 3.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MAI-Thinking-1 | Mistral Large 3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MAI-Thinking-1Not available | Mistral Large 3$0.5 input / $1.5 output | A complete price comparison is not available. |
| Generation speedtokens per second | MAI-Thinking-1Not available | Mistral Large 348 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MAI-Thinking-1Not available | Mistral Large 31.04 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MAI-Thinking-1256K | Mistral Large 3128K | MAI-Thinking-1 lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding5 benchmarks
Reasoning3 benchmarks
Knowledge10 benchmarks
| Benchmark | MAI-Thinking-1 | Mistral Large 3 | Result |
|---|---|---|---|
| GPQASource | 84.2% | — | Not comparable |
| GPQA-DSource | 84.2% | — | Not comparable |
| MMLU-ProSource | 85% | — | Not comparable |
| SimpleQASource | 31% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 15.9% | Not comparable |
| AA-GPQA DiamondSource | — | 68.0% | Not comparable |
| AA-HLESource | — | 4.1% | Not comparable |
| AA-Omniscience IndexSource | — | -39.4% | Not comparable |
| AA-Omniscience AccuracySource | — | 24.1% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 83.7% | Not comparable |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | MAI-Thinking-1 | Mistral Large 3 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | — | 55.7% | Not comparable |
Frequently Asked Questions (3)
Can I compare MAI-Thinking-1 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 MAI-Thinking-1 and Mistral Large 3 today?
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.
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