Model comparison
DeepSeek V3.2 vs Mistral Medium 3.5 128B
Head-to-head evidence from 12 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3.2 #88 (Supported); Mistral Medium 3.5 128B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and Mistral Medium 3.5 128B share 12 comparable benchmark results. 1 of 8 categories are comparable. 7 results are unique to DeepSeek V3.2; 13 to Mistral Medium 3.5 128B.
Updated July 27, 2026- Shared results
- 12
- DeepSeek V3.2 only
- 7
- Mistral Medium 3.5 128B only
- 13
- Comparable categories
- 1 / 8
Treat this as a split decision. DeepSeek V3.2 makes more sense if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model; Mistral Medium 3.5 128B is the better fit if coding is the priority or you need the larger 256K context window.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 5 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
DeepSeek V3.2 and Mistral Medium 3.5 128B finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Mistral Medium 3.5 128B is also the more expensive model on tokens at $1.50 input / $7.50 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 17.9x on output cost alone. Mistral Medium 3.5 128B is the reasoning model in the pair, while DeepSeek V3.2 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Mistral Medium 3.5 128B gives you the larger context window at 256K, compared with 128K 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 Medium 3.5 128B |
|---|---|---|---|
| Coding | DeepSeek V3.260.9 | Margin→ 16.7 | Mistral Medium 3.5 128B77.6 |
| Math | DeepSeek V3.217.1 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | Mistral Medium 3.5 128B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | Mistral Medium 3.5 128B$1.5 input / $7.5 output | DeepSeek V3.2 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | Mistral Medium 3.5 128BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | Mistral Medium 3.5 128BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | Mistral Medium 3.5 128B256K | Mistral Medium 3.5 128B lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | DeepSeek V3.2 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| Claw-EvalSource | 40.2% | — | Not comparable |
| VITA-BenchSource | 18.5% | — | Not comparable |
| τ²-bench resultsSource | 78.9% | 94.2% | Mistral Medium 3.5 128B leads |
| Gert LabsSource | 29.57% | 39.10% | Mistral Medium 3.5 128B leads |
| τ³-bench resultsSource | — | 91.4% | Not comparable |
| AA Agentic IndexSource | — | 19.0% | Not comparable |
| GDPval-AASource | — | 21.6% | Not comparable |
| GDPval-AASource | — | 933 | Not comparable |
| AA EnterpriseOps-GymSource | — | 33.7% | Not comparable |
| AA Harvey LABSource | — | 69.1% | Not comparable |
| terminalBenchHardSource | — | 33.3% | Not comparable |
| AA BriefcaseSource | — | 516 | Not comparable |
| AA Tau3 BankingSource | — | 14.4% | Not comparable |
CodingMistral Medium 3.5 128B wins5 benchmarks
Reasoning2 benchmarks
Knowledge7 benchmarks
| Benchmark | DeepSeek V3.2 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 29.9% | Mistral Medium 3.5 128B leads |
| AA-GPQA DiamondSource | 75.1% | 74.8% | DeepSeek V3.2 leads |
| AA-HLESource | 10.5% | 12.8% | Mistral Medium 3.5 128B leads |
| AA-Omniscience IndexSource | -46.7% | -36.3% | Mistral Medium 3.5 128B leads |
| AA-Omniscience AccuracySource | 24.2% | 25.1% | Mistral Medium 3.5 128B leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 82.0% | Mistral Medium 3.5 128B leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Math2 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | 68.8% | Mistral Medium 3.5 128B leads |
Frequently Asked Questions (2)
Which is better, DeepSeek V3.2 or Mistral Medium 3.5 128B?
DeepSeek V3.2 and Mistral Medium 3.5 128B are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for coding, DeepSeek V3.2 or Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 60.9. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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