Model comparison
DeepSeek V3.2 vs Mistral Large 3
Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3.2 #82 (Supported); Mistral Large 3 #113 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and Mistral Large 3 share 11 comparable benchmark results. 0 of 8 categories are comparable. 8 results are unique to DeepSeek V3.2; 5 to Mistral Large 3.
Updated July 20, 2026- Shared results
- 11
- DeepSeek V3.2 only
- 8
- Mistral Large 3 only
- 5
- Comparable categories
- 0 / 8
Benchmark data for DeepSeek V3.2 and Mistral Large 3 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 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.
Mistral Large 3 is priced at $0.50 input / $1.50 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2.
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 | DeepSeek V3.2 | Δ | Mistral Large 3 |
|---|---|---|---|
| Coding | DeepSeek V3.260.9 | MarginNo overlap | Mistral Large 3Not measured |
| Math | DeepSeek V3.217.1 | MarginNo overlap | Mistral Large 3Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | Mistral Large 3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | Mistral Large 3$0.5 input / $1.5 output | DeepSeek V3.2 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | Mistral Large 348 tok/s | Mistral Large 3 has the higher measured throughput. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | Mistral Large 31.04 s | Mistral Large 3 reaches the first token sooner. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | Mistral Large 3128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic7 benchmarks
| Benchmark | DeepSeek V3.2 | Mistral Large 3 | Result |
|---|---|---|---|
| Claw-EvalSource | 40.2% | — | Not comparable |
| VITA-BenchSource | 18.5% | — | Not comparable |
| τ²-bench resultsSource | 78.9% | 24.6% | DeepSeek V3.2 leads |
| Gert LabsSource | 29.57% | — | Not comparable |
| AA Agentic IndexSource | — | 5.5% | Not comparable |
| GDPval-AASource | — | 6.6% | Not comparable |
| GDPval-AASource | — | 633 | Not comparable |
Coding4 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | DeepSeek V3.2 | Mistral Large 3 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 15.9% | DeepSeek V3.2 leads |
| AA-GPQA DiamondSource | 75.1% | 68.0% | DeepSeek V3.2 leads |
| AA-HLESource | 10.5% | 4.1% | DeepSeek V3.2 leads |
| AA-Omniscience IndexSource | -46.7% | -39.4% | Mistral Large 3 leads |
| AA-Omniscience AccuracySource | 24.2% | 24.1% | DeepSeek V3.2 leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 83.7% | Mistral Large 3 leads |
Math2 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | Mistral Large 3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | 36.2% | DeepSeek V3.2 leads |
Frequently Asked Questions (3)
Can I compare DeepSeek V3.2 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 DeepSeek V3.2 and Mistral Large 3 today?
DeepSeek V3.2: $0.28 input / $0.42 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.
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