Model comparison
GPT-5.4 nano vs MiniMax M3
Head-to-head evidence from 21 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GPT-5.4 nano unranked; MiniMax M3 #18
BenchAlign evidence: GPT-5.4 nano supported; MiniMax M3 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.4 nano and MiniMax M3 share 21 comparable benchmark results. 3 of 8 categories are comparable. 9 results are unique to GPT-5.4 nano; 24 to MiniMax M3.
Updated July 16, 2026- Shared results
- 21
- GPT-5.4 nano only
- 9
- MiniMax M3 only
- 24
- Comparable categories
- 3 / 8
Pick MiniMax M3 if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if multimodal & grounded is the priority or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 21 shared benchmark results across 6 evidence categories; 3 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 60. 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 21. The single biggest benchmark swing on the page is OSWorld-Verified, 39% to 70.1%. GPT-5.4 nano does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
GPT-5.4 nano is also the more expensive model on tokens at $0.20 input / $1.25 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M3. GPT-5.4 nano is the reasoning model in the pair, while MiniMax M3 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. MiniMax M3 gives you the larger context window at 1M, compared with 400K for GPT-5.4 nano.
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-5.4 nano | Δ | MiniMax M3 |
|---|---|---|---|
| Math | GPT-5.4 nano21.0 | Margin→ 64.7 | MiniMax M385.7 |
| Agentic | GPT-5.4 nano42.9 | Margin→ 29.4 | MiniMax M372.3 |
| Multimodal | GPT-5.4 nano66.1 | Margin← 1.2 | MiniMax M364.9 |
| Coding | GPT-5.4 nanoNot measured | MarginNo overlap | MiniMax M372.2 |
| Knowledge | GPT-5.4 nano43.8 | MarginNo overlap | MiniMax M3Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
OSWorld-Verified
AgenticA 39%B 70.1%Winner: MiniMax M3Δ 31.1OSWorld-Verified: GPT-5.4 nano scored 39%; MiniMax M3 scored 70.1%. MiniMax M3 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 46.3%B 66%Winner: MiniMax M3Δ 19.7Terminal-Bench 2.0: GPT-5.4 nano scored 46.3%; MiniMax M3 scored 66%. MiniMax M3 wins this benchmark. - Source ↗
MMMU-Pro
MultimodalA 66.1%B 78.1%Winner: MiniMax M3Δ 12MMMU-Pro: GPT-5.4 nano scored 66.1%; MiniMax M3 scored 78.1%. MiniMax M3 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.4 nano | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.4 nano$0.2 input / $1.25 output | MiniMax M3$0.3 input / $1.2 output | GPT-5.4 nano has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.4 nano191 tok/s | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.4 nano3.64 s | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.4 nano400K | MiniMax M31M | MiniMax M3 lists the larger context window. |
Benchmark Deep Dive
AgenticMiniMax M3 wins18 benchmarks
| Benchmark | GPT-5.4 nano | MiniMax M3 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 46.3% | 66% | MiniMax M3 leads |
| OSWorld-VerifiedSource | 39% | 70.1% | MiniMax M3 leads |
| MCP AtlasSource | 56.1% | 74.2% | MiniMax M3 leads |
| ToolathlonSource | 35.5% | — | Not comparable |
| τ²-bench resultsSource | 76% | 88.9% | MiniMax M3 leads |
| AA Agentic IndexSource | 27.5% | 35.4% | MiniMax M3 leads |
| APEX-Agents-AASource | 24.9% | — | Not comparable |
| GDPval-AASource | 30.0% | 44.7% | MiniMax M3 leads |
| GDPval-AASource | 1100 | 1395 | MiniMax M3 leads |
| BrowseCompSource | — | 83.5% | 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 |
Coding12 benchmarks
| Benchmark | GPT-5.4 nano | MiniMax M3 | Result |
|---|---|---|---|
| Vibe Code BenchSource | 26.10% | — | Not comparable |
| AA Coding IndexSource | 56.1% | 58.6% | MiniMax M3 leads |
| Terminal-Bench HardSource | 42.4% | 42.4% | Tie |
| AA-SciCodeSource | 46.9% | 45.4% | GPT-5.4 nano 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
Knowledge10 benchmarks
| Benchmark | GPT-5.4 nano | MiniMax M3 | Result |
|---|---|---|---|
| GPQASource | 82.8% | — | Not comparable |
| HLESource | 37.7% | — | Not comparable |
| HLE w/o toolsSource | 24.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 38.2% | 44.4% | MiniMax M3 leads |
| AA-GPQA DiamondSource | 81.7% | 92.9% | MiniMax M3 leads |
| AA-HLESource | 26.5% | 37.1% | MiniMax M3 leads |
| AA-Omniscience IndexSource | -29.5% | 1.4% | MiniMax M3 leads |
| AA-Omniscience AccuracySource | 25.4% | 15.0% | GPT-5.4 nano leads |
| AA-Omniscience Hallucination RateSource | 73.6% | 16.1% | MiniMax M3 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
MathMiniMax M3 wins3 benchmarks
MultimodalGPT-5.4 nano wins8 benchmarks
| Benchmark | GPT-5.4 nano | MiniMax M3 | Result |
|---|---|---|---|
| MMMU-ProSource | 66.1% | 78.1% | MiniMax M3 leads |
| MMMU-Pro w/ PythonSource | 69.5% | — | Not comparable |
| AA-MMMU-ProSource | 65.4% | 78.6% | MiniMax M3 leads |
| OfficeQA ProSource | — | 45.1% | Not comparable |
| OmniDocBench 1.5Source | — | 91.6% | Not comparable |
| VideoMMMUSource | — | 84.6% | Not comparable |
| Video-MME (with subtitle)Source | — | 85.4% | Not comparable |
| Design Arena WebsiteSource | — | 1294 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.4 nano | MiniMax M3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.9% | 82.9% | MiniMax M3 leads |
Frequently Asked Questions (4)
Which is better, GPT-5.4 nano or MiniMax M3?
MiniMax M3 is ahead on BenchLM's provisional leaderboard, 70 to 60. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 39% and 70.1%.
Which is better for math, GPT-5.4 nano or MiniMax M3?
MiniMax M3 has the edge for math in this comparison, averaging 85.7 versus 21. GPT-5.4 nano stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GPT-5.4 nano or MiniMax M3?
MiniMax M3 has the edge for agentic tasks in this comparison, averaging 72.3 versus 42.9. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, GPT-5.4 nano or MiniMax M3?
GPT-5.4 nano has the edge for multimodal and grounded tasks in this comparison, averaging 66.1 versus 64.9. Inside this category, AA-MMMU-Pro is the benchmark that creates the most daylight between them.
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