Model comparison
Mistral Large 3 vs SWE-1.7
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use BenchLM's provisional ranking lane.
Evidence parity. Mistral Large 3 and SWE-1.7 share 0 comparable benchmark results. 0 of 8 categories are comparable. 17 results are unique to Mistral Large 3; 4 to SWE-1.7.
Updated July 14, 2026- Shared results
- 0
- Mistral Large 3 only
- 17
- SWE-1.7 only
- 4
- Comparable categories
- 0 / 8
Benchmark data for Mistral Large 3 and SWE-1.7 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.
SWE-1.7 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 | Mistral Large 3 | SWE-1.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Mistral Large 3$0.5 input / $1.5 output | SWE-1.7Not available | A complete price comparison is not available. |
| Generation speedtokens per second | Mistral Large 348 tok/s | SWE-1.7Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Mistral Large 31.04 s | SWE-1.7Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Mistral Large 3128K | SWE-1.7256K | SWE-1.7 lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding6 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Mistral Large 3 | SWE-1.7 | Result |
|---|---|---|---|
| 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 |
Multimodal1 benchmarks
| Benchmark | Mistral Large 3 | SWE-1.7 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 55.7% | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Mistral Large 3 | SWE-1.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 36.2% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Mistral Large 3 and SWE-1.7 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 Mistral Large 3 and SWE-1.7 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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