Model comparison
DeepSeek V4 Pro (Max) vs Mistral Medium 3.5 128B
Head-to-head evidence from 22 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro (Max) #58 (Estimated); 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 V4 Pro (Max) and Mistral Medium 3.5 128B share 22 comparable benchmark results. 1 of 8 categories are comparable. 26 results are unique to DeepSeek V4 Pro (Max); 3 to Mistral Medium 3.5 128B.
Updated July 27, 2026- Shared results
- 22
- DeepSeek V4 Pro (Max) only
- 26
- Mistral Medium 3.5 128B only
- 3
- Comparable categories
- 1 / 8
Treat this as a split decision. DeepSeek V4 Pro (Max) makes more sense if you want the cheaper token bill or you need the larger 1M context window; Mistral Medium 3.5 128B is the better fit if coding is the priority.
Confidence note. This is a partial-evidence comparison with 22 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 V4 Pro (Max) 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 $0.43 input / $0.87 output per 1M tokens for DeepSeek V4 Pro (Max). That is roughly 8.6x on output cost alone. DeepSeek V4 Pro (Max) gives you the larger context window at 1M, compared with 256K for Mistral Medium 3.5 128B.
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 V4 Pro (Max) | Δ | Mistral Medium 3.5 128B |
|---|---|---|---|
| Coding | DeepSeek V4 Pro (Max)70.9 | Margin→ 6.7 | Mistral Medium 3.5 128B77.6 |
| Agentic | DeepSeek V4 Pro (Max)74.5 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Knowledge | DeepSeek V4 Pro (Max)60.1 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Math | DeepSeek V4 Pro (Max)95.2 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 80.6%B 77.6%Winner: DeepSeek V4 Pro (Max)Δ 3SWE-bench Verified: DeepSeek V4 Pro (Max) scored 80.6%; Mistral Medium 3.5 128B scored 77.6%. DeepSeek V4 Pro (Max) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Pro (Max) | Mistral Medium 3.5 128B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro (Max)$0.435 input / $0.87 output | Mistral Medium 3.5 128B$1.5 input / $7.5 output | DeepSeek V4 Pro (Max) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 Pro (Max)Not available | Mistral Medium 3.5 128BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Pro (Max)Not available | Mistral Medium 3.5 128BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro (Max)1M | Mistral Medium 3.5 128B256K | DeepSeek V4 Pro (Max) lists the larger context window. |
Benchmark Deep Dive
Agentic19 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 67.9% | — | Not comparable |
| BrowseCompSource | 83.4% | — | Not comparable |
| HLE w/ toolsSource | 48.2% | — | Not comparable |
| MCP AtlasSource | 73.6% | — | Not comparable |
| GDPval-AASource | 1306 | 933 | DeepSeek V4 Pro (Max) leads |
| ToolathlonSource | 51.8% | — | Not comparable |
| AA Agentic IndexSource | 36.4% | 19.0% | DeepSeek V4 Pro (Max) leads |
| APEX-Agents-AASource | 24.3% | — | Not comparable |
| τ²-bench resultsSource | 96.2% | 94.2% | DeepSeek V4 Pro (Max) leads |
| GDPval-AASource | 40.3% | 21.6% | DeepSeek V4 Pro (Max) leads |
| AA BriefcaseSource | 930 | 516 | DeepSeek V4 Pro (Max) leads |
| AA EnterpriseOps-GymSource | 40.4% | 33.7% | DeepSeek V4 Pro (Max) leads |
| AA Harvey LABSource | 84.4% | 69.1% | DeepSeek V4 Pro (Max) leads |
| AA ITBenchSource | 38.3% | — | Not comparable |
| AA Tau3 BankingSource | 25.8% | 14.4% | DeepSeek V4 Pro (Max) leads |
| terminalBenchHardSource | 46.2% | 33.3% | DeepSeek V4 Pro (Max) leads |
| aaTerminalBench21Source | 64% | — | Not comparable |
| τ³-bench resultsSource | — | 91.4% | Not comparable |
| Gert LabsSource | — | 39.10% | Not comparable |
CodingMistral Medium 3.5 128B wins8 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| CodeforcesSource | 3206.0 | — | Not comparable |
| SWE-bench VerifiedSource | 80.6% | 77.6% | DeepSeek V4 Pro (Max) leads |
| SWE-bench ProSource | 55.4% | — | Not comparable |
| SWE MultilingualSource | 76.2% | — | Not comparable |
| Terminal-Bench 2.0Source | 67.9% | — | Not comparable |
| Vibe Code BenchSource | 49.93% | — | Not comparable |
| AA Coding IndexSource | 59.4% | 46.9% | DeepSeek V4 Pro (Max) leads |
| AA-SciCodeSource | 50.0% | 39.6% | DeepSeek V4 Pro (Max) leads |
Reasoning4 benchmarks
Knowledge13 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| MMLU-ProSource | 87.5% | — | Not comparable |
| SimpleQASource | 57.9% | — | Not comparable |
| Chinese-SimpleQASource | 84.4% | — | Not comparable |
| GPQASource | 90.1% | — | Not comparable |
| GPQA-DSource | 90.1% | — | Not comparable |
| HLESource | 37.7% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 44.3% | 29.9% | DeepSeek V4 Pro (Max) leads |
| AA-GPQA DiamondSource | 88.8% | 74.8% | DeepSeek V4 Pro (Max) leads |
| AA-HLESource | 35.9% | 12.8% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience IndexSource | -10.0% | -36.3% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience AccuracySource | 43.3% | 25.1% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience Hallucination RateSource | 94.0% | 82.0% | Mistral Medium 3.5 128B leads |
| AA Openness IndexSource | 50.0% | 33.3% | DeepSeek V4 Pro (Max) leads |
Math4 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V4 Pro (Max) | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.5% | 68.8% | DeepSeek V4 Pro (Max) leads |
Frequently Asked Questions (2)
Which is better, DeepSeek V4 Pro (Max) or Mistral Medium 3.5 128B?
DeepSeek V4 Pro (Max) 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 V4 Pro (Max) or Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 70.9. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
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