Model comparison
GPT-4.1 nano vs MiniMax M3
Head-to-head evidence from 18 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GPT-4.1 nano unranked; MiniMax M3 #18
BenchAlign evidence: GPT-4.1 nano estimated; MiniMax M3 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-4.1 nano and MiniMax M3 share 18 comparable benchmark results. 1 of 8 categories are comparable. 4 results are unique to GPT-4.1 nano; 27 to MiniMax M3.
Updated July 16, 2026- Shared results
- 18
- GPT-4.1 nano only
- 4
- MiniMax M3 only
- 27
- Comparable categories
- 1 / 8
Pick MiniMax M3 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 18 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
MiniMax M3 is clearly ahead on the provisional aggregate, 70 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M3's sharpest advantage is in mathematics, where it averages 85.7 against 1.
MiniMax M3 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 3.0x on output cost alone.
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 | MiniMax M3 |
|---|---|---|---|
| Math | GPT-4.1 nano1.0 | Margin→ 84.7 | MiniMax M385.7 |
| Agentic | GPT-4.1 nanoNot measured | MarginNo overlap | MiniMax M372.3 |
| Coding | GPT-4.1 nanoNot measured | MarginNo overlap | MiniMax M372.2 |
| Knowledge | GPT-4.1 nano50.3 | MarginNo overlap | MiniMax M3Not measured |
| Multimodal | GPT-4.1 nanoNot measured | MarginNo overlap | MiniMax M364.9 |
| Inst. Following | GPT-4.1 nano83.2 | MarginNo overlap | MiniMax M3Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-4.1 nano | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4.1 nano$0.1 input / $0.4 output | MiniMax M3$0.3 input / $1.2 output | GPT-4.1 nano has the lower combined listed price. |
| Generation speedtokens per second | GPT-4.1 nano181 tok/s | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-4.1 nano0.63 s | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-4.1 nano1M | MiniMax M31M | Listed context windows are equal. |
Benchmark Deep Dive
Agentic16 benchmarks
| Benchmark | GPT-4.1 nano | MiniMax M3 | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.2% | 35.4% | MiniMax M3 leads |
| τ²-bench resultsSource | 17.3% | 88.9% | MiniMax M3 leads |
| GDPval-AASource | 0.0% | 44.7% | MiniMax M3 leads |
| GDPval-AASource | 41 | 1395 | MiniMax M3 leads |
| Terminal-Bench 2.0Source | — | 66% | Not comparable |
| BrowseCompSource | — | 83.5% | Not comparable |
| OSWorld-VerifiedSource | — | 70.1% | Not comparable |
| MCP AtlasSource | — | 74.2% | Not comparable |
| Claw-EvalSource | — | 74.5% | Not comparable |
| GDPval rubricsSource | — | 74.7% | Not comparable |
| BankerToolBenchSource | — | 76.1% | Not comparable |
| ResearchClawBenchSource | — | 19.8% | Not comparable |
| OSWorld 2.0Source | — | 4.6% | Not comparable |
| AA BriefcaseSource | — | 1110 | Not comparable |
| AA EnterpriseOps-GymSource | — | 32.1% | Not comparable |
| AA Harvey LABSource | — | 6.7% | Not comparable |
Coding11 benchmarks
| Benchmark | GPT-4.1 nano | MiniMax M3 | Result |
|---|---|---|---|
| AA Coding IndexSource | 11.1% | 58.6% | MiniMax M3 leads |
| Terminal-Bench HardSource | 3.8% | 42.4% | MiniMax M3 leads |
| AA-SciCodeSource | 25.9% | 45.4% | MiniMax M3 leads |
| SWE-bench VerifiedSource | — | 80.5% | Not comparable |
| SWE-bench ProSource | — | 59% | Not comparable |
| Terminal-Bench 2.0Source | — | 66.0% | Not comparable |
| NL2RepoSource | — | 42.1% | Not comparable |
| VIBE V2Source | — | 50.1% | Not comparable |
| SVG-BenchSource | — | 63.7% | Not comparable |
| KernelBench HardSource | — | 28.8% | Not comparable |
| AA Terminal-Bench 2.1Source | — | 65.2% | Not comparable |
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GPT-4.1 nano | MiniMax M3 | Result |
|---|---|---|---|
| MMLUSource | 80.1% | — | Not comparable |
| GPQASource | 50.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 9.6% | 44.4% | MiniMax M3 leads |
| AA-GPQA DiamondSource | 51.2% | 92.9% | MiniMax M3 leads |
| AA-HLESource | 3.9% | 37.1% | MiniMax M3 leads |
| AA-Omniscience IndexSource | -56.4% | 1.4% | MiniMax M3 leads |
| AA-Omniscience AccuracySource | 13.3% | 15.0% | MiniMax M3 leads |
| AA-Omniscience Hallucination RateSource | 80.4% | 16.1% | MiniMax M3 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
MathMiniMax M3 wins2 benchmarks
Multimodal7 benchmarks
| Benchmark | GPT-4.1 nano | MiniMax M3 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 40.1% | 78.6% | MiniMax M3 leads |
| Design Arena WebsiteSource | 1007 | 1294 | MiniMax M3 leads |
| OfficeQA ProSource | — | 45.1% | Not comparable |
| OmniDocBench 1.5Source | — | 91.6% | Not comparable |
| MMMU-ProSource | — | 78.1% | Not comparable |
| VideoMMMUSource | — | 84.6% | Not comparable |
| Video-MME (with subtitle)Source | — | 85.4% | Not comparable |
Frequently Asked Questions (2)
Which is better, GPT-4.1 nano or MiniMax M3?
MiniMax M3 is ahead on BenchLM's provisional leaderboard, 70 to 30.
Which is better for math, GPT-4.1 nano or MiniMax M3?
MiniMax M3 has the edge for math in this comparison, averaging 85.7 versus 1. GPT-4.1 nano stays close enough that the answer can still flip depending on your workload.
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