Model comparison
GPT-4.1 mini vs Mistral Medium 3.5 128B
Head-to-head evidence from 17 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-4.1 mini #160 (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. GPT-4.1 mini and Mistral Medium 3.5 128B share 17 comparable benchmark results. 1 of 8 categories are comparable. 5 results are unique to GPT-4.1 mini; 8 to Mistral Medium 3.5 128B.
Updated July 27, 2026- Shared results
- 17
- GPT-4.1 mini only
- 5
- Mistral Medium 3.5 128B only
- 8
- Comparable categories
- 1 / 8
Treat this as a split decision. GPT-4.1 mini 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 or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 17 shared benchmark results across 6 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
GPT-4.1 mini 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 $0.40 input / $1.60 output per 1M tokens for GPT-4.1 mini. That is roughly 4.7x on output cost alone. Mistral Medium 3.5 128B is the reasoning model in the pair, while GPT-4.1 mini 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. GPT-4.1 mini 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 | GPT-4.1 mini | Δ | Mistral Medium 3.5 128B |
|---|---|---|---|
| Coding | GPT-4.1 mini23.6 | Margin→ 54.0 | Mistral Medium 3.5 128B77.6 |
| Knowledge | GPT-4.1 mini64.2 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Math | GPT-4.1 mini4.5 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Inst. Following | GPT-4.1 mini88.5 | 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 23.6%B 77.6%Winner: Mistral Medium 3.5 128BΔ 54SWE-bench Verified: GPT-4.1 mini scored 23.6%; Mistral Medium 3.5 128B scored 77.6%. Mistral Medium 3.5 128B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-4.1 mini | Mistral Medium 3.5 128B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4.1 mini$0.4 input / $1.6 output | Mistral Medium 3.5 128B$1.5 input / $7.5 output | GPT-4.1 mini has the lower combined listed price. |
| Generation speedtokens per second | GPT-4.1 mini80 tok/s | Mistral Medium 3.5 128BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-4.1 mini0.76 s | Mistral Medium 3.5 128BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-4.1 mini1M | Mistral Medium 3.5 128B256K | GPT-4.1 mini lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | GPT-4.1 mini | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.7% | 19.0% | Mistral Medium 3.5 128B leads |
| τ²-bench resultsSource | 52.9% | 94.2% | Mistral Medium 3.5 128B leads |
| GDPval-AASource | 0.4% | 21.6% | Mistral Medium 3.5 128B leads |
| GDPval-AASource | 508 | 933 | Mistral Medium 3.5 128B leads |
| τ³-bench resultsSource | — | 91.4% | Not comparable |
| Gert LabsSource | — | 39.10% | 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 wins3 benchmarks
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GPT-4.1 mini | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| MMLUSource | 87.5% | — | Not comparable |
| GPQASource | 64.2% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 14.8% | 29.9% | Mistral Medium 3.5 128B leads |
| AA-GPQA DiamondSource | 66.4% | 74.8% | Mistral Medium 3.5 128B leads |
| AA-HLESource | 4.6% | 12.8% | Mistral Medium 3.5 128B leads |
| AA-Omniscience IndexSource | -50.1% | -36.3% | Mistral Medium 3.5 128B leads |
| AA-Omniscience AccuracySource | 17.5% | 25.1% | Mistral Medium 3.5 128B leads |
| AA-Omniscience Hallucination RateSource | 82.0% | 82.0% | Tie |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Math1 benchmarks
| Benchmark | GPT-4.1 mini | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 4.483% | — | Not comparable |
Multimodal2 benchmarks
Frequently Asked Questions (2)
Which is better, GPT-4.1 mini or Mistral Medium 3.5 128B?
GPT-4.1 mini 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, GPT-4.1 mini or Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 23.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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