Model comparison
GPT-4.1 nano vs Mistral Medium 3.5 128B
Head-to-head evidence from 16 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-4.1 nano #170 (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 nano and Mistral Medium 3.5 128B share 16 comparable benchmark results. 0 of 8 categories are comparable. 5 results are unique to GPT-4.1 nano; 9 to Mistral Medium 3.5 128B.
Updated July 28, 2026- Shared results
- 16
- GPT-4.1 nano only
- 5
- Mistral Medium 3.5 128B only
- 9
- Comparable categories
- 0 / 8
Benchmark data for GPT-4.1 nano and Mistral Medium 3.5 128B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 16 shared benchmark results across 6 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 Medium 3.5 128B is priced at $1.50 input / $7.50 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. GPT-4.1 nano has 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 nano | Δ | Mistral Medium 3.5 128B |
|---|---|---|---|
| Coding | GPT-4.1 nanoNot measured | MarginNo overlap | Mistral Medium 3.5 128B77.6 |
| Knowledge | GPT-4.1 nano50.3 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Math | GPT-4.1 nano1.0 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Inst. Following | GPT-4.1 nano83.2 | 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 | GPT-4.1 nano | Mistral Medium 3.5 128B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4.1 nano$0.1 input / $0.4 output | Mistral Medium 3.5 128B$1.5 input / $7.5 output | GPT-4.1 nano has the lower combined listed price. |
| Generation speedtokens per second | GPT-4.1 nano181 tok/s | Mistral Medium 3.5 128BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-4.1 nano0.63 s | Mistral Medium 3.5 128BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-4.1 nano1M | Mistral Medium 3.5 128B256K | GPT-4.1 nano lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | GPT-4.1 nano | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.2% | 19.0% | Mistral Medium 3.5 128B leads |
| τ²-bench resultsSource | 17.3% | 94.2% | Mistral Medium 3.5 128B leads |
| GDPval-AASource | 0.0% | 21.6% | Mistral Medium 3.5 128B leads |
| GDPval-AASource | 63 | 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 |
Coding3 benchmarks
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GPT-4.1 nano | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| MMLUSource | 80.1% | — | Not comparable |
| GPQASource | 50.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 9.6% | 29.9% | Mistral Medium 3.5 128B leads |
| AA-GPQA DiamondSource | 51.2% | 74.8% | Mistral Medium 3.5 128B leads |
| AA-HLESource | 3.9% | 12.8% | Mistral Medium 3.5 128B leads |
| AA-Omniscience IndexSource | -56.4% | -36.3% | Mistral Medium 3.5 128B leads |
| AA-Omniscience AccuracySource | 13.3% | 25.1% | Mistral Medium 3.5 128B leads |
| AA-Omniscience Hallucination RateSource | 80.4% | 82.0% | GPT-4.1 nano leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Math1 benchmarks
| Benchmark | GPT-4.1 nano | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 1.034% | — | Not comparable |
Multimodal2 benchmarks
Frequently Asked Questions (3)
Can I compare GPT-4.1 nano and Mistral Medium 3.5 128B 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 GPT-4.1 nano and Mistral Medium 3.5 128B today?
GPT-4.1 nano: $0.10 input / $0.40 output per 1M tokens Mistral Medium 3.5 128B: $1.50 input / $7.50 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Related Comparisons
Explore More
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
One email each week. Unsubscribe anytime.