Model comparison
GPT-5.5 vs MiniMax M3
Head-to-head evidence from 32 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GPT-5.5 #3; MiniMax M3 #18
BenchAlign evidence: GPT-5.5 supported; MiniMax M3 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.5 and MiniMax M3 share 32 comparable benchmark results. 4 of 8 categories are comparable. 25 results are unique to GPT-5.5; 13 to MiniMax M3.
Updated July 16, 2026- Shared results
- 32
- GPT-5.5 only
- 25
- MiniMax M3 only
- 13
- Comparable categories
- 4 / 8
Pick GPT-5.5 if you want the stronger benchmark profile. MiniMax M3 only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 32 shared benchmark results across 6 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.5 is clearly ahead on the provisional aggregate, 78 to 70. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.5's sharpest advantage is in agentic, where it averages 81.6 against 72.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 82% to 66%. MiniMax M3 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
GPT-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M3. That is roughly 25.0x on output cost alone. GPT-5.5 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.
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-5.5 | Δ | MiniMax M3 |
|---|---|---|---|
| Math | GPT-5.547.6 | Margin→ 38.1 | MiniMax M385.7 |
| Coding | GPT-5.558.6 | Margin→ 13.6 | MiniMax M372.2 |
| Agentic | GPT-5.581.6 | Margin← 9.3 | MiniMax M372.3 |
| Multimodal | GPT-5.570.4 | Margin← 5.5 | MiniMax M364.9 |
| Reasoning | GPT-5.585.0 | MarginNo overlap | MiniMax M3Not measured |
| Knowledge | GPT-5.557.8 | MarginNo overlap | MiniMax M3Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 82%B 66%Winner: GPT-5.5Δ 16Terminal-Bench 2.0: GPT-5.5 scored 82%; MiniMax M3 scored 66%. GPT-5.5 wins this benchmark. - Source ↗
OfficeQA Pro
MultimodalA 54.1%B 45.1%Winner: GPT-5.5Δ 9OfficeQA Pro: GPT-5.5 scored 54.1%; MiniMax M3 scored 45.1%. GPT-5.5 wins this benchmark. - Source ↗
OSWorld-Verified
AgenticA 78.7%B 70.1%Winner: GPT-5.5Δ 8.6OSWorld-Verified: GPT-5.5 scored 78.7%; MiniMax M3 scored 70.1%. GPT-5.5 wins this benchmark. - Source ↗
MMMU-Pro
MultimodalA 81.2%B 78.1%Winner: GPT-5.5Δ 3.1MMMU-Pro: GPT-5.5 scored 81.2%; MiniMax M3 scored 78.1%. GPT-5.5 wins this benchmark. - Source ↗
BrowseComp
AgenticA 84.4%B 83.5%Winner: GPT-5.5Δ 0.9BrowseComp: GPT-5.5 scored 84.4%; MiniMax M3 scored 83.5%. GPT-5.5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.5 | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5$5 input / $30 output | MiniMax M3$0.3 input / $1.2 output | MiniMax M3 has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.5Not available | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5Not available | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.51M | MiniMax M31M | Listed context windows are equal. |
Benchmark Deep Dive
AgenticGPT-5.5 wins25 benchmarks
| Benchmark | GPT-5.5 | MiniMax M3 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 82% | 66% | GPT-5.5 leads |
| CyberGymSource | 81.8% | — | Not comparable |
| BrowseCompSource | 84.4% | 83.5% | GPT-5.5 leads |
| OSWorld-VerifiedSource | 78.7% | 70.1% | GPT-5.5 leads |
| MCP AtlasSource | 75.3% | 74.2% | GPT-5.5 leads |
| ToolathlonSource | 55.6% | — | Not comparable |
| τ²-bench resultsSource | 93.9% | 88.9% | GPT-5.5 leads |
| AA Agentic IndexSource | 44.9% | 35.4% | GPT-5.5 leads |
| APEX-Agents-AASource | 37.7% | — | Not comparable |
| GDPval-AASource | 49.7% | 44.7% | GPT-5.5 leads |
| GDPval-AASource | 1493 | 1395 | GPT-5.5 leads |
| Gert LabsSource | 72.93% | — | Not comparable |
| ResearchClawBenchSource | 17.0% | 19.8% | MiniMax M3 leads |
| OSWorld 2.0Source | 13.0% | 4.6% | GPT-5.5 leads |
| JobBenchSource | 42.7% | — | Not comparable |
| ExploitGymSource | 13.4% | — | Not comparable |
| AA BriefcaseSource | 1154 | 1110 | GPT-5.5 leads |
| AA AutomationBenchSource | 42.1% | — | Not comparable |
| AA EnterpriseOps-GymSource | 46.6% | 32.1% | GPT-5.5 leads |
| AA Harvey LABSource | 4.2% | 6.7% | MiniMax M3 leads |
| AA ITBenchSource | 45.8% | — | Not comparable |
| AA Tau3 BankingSource | 31.3% | — | Not comparable |
| Claw-EvalSource | — | 74.5% | Not comparable |
| GDPval rubricsSource | — | 74.7% | Not comparable |
| BankerToolBenchSource | — | 76.1% | Not comparable |
CodingMiniMax M3 wins16 benchmarks
| Benchmark | GPT-5.5 | MiniMax M3 | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.6% | 59% | MiniMax M3 leads |
| Terminal-Bench 2.0Source | 82.0% | 66.0% | GPT-5.5 leads |
| Vibe Code BenchSource | 69.85% | — | Not comparable |
| React Native EvalsSource | 84.7% | — | Not comparable |
| cursorBench31Source | 59.2% | — | Not comparable |
| cursorBench32Source | 58.4% | — | Not comparable |
| AA Coding IndexSource | 74.9% | 58.6% | GPT-5.5 leads |
| Terminal-Bench HardSource | 60.6% | 42.4% | GPT-5.5 leads |
| AA-SciCodeSource | 56.1% | 45.4% | GPT-5.5 leads |
| FrontierCodeSource | 43.0% | — | Not comparable |
| AA Terminal-Bench 2.1Source | 84.3% | 65.2% | GPT-5.5 leads |
| SWE-bench VerifiedSource | — | 80.5% | 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 |
Reasoning5 benchmarks
Knowledge11 benchmarks
| Benchmark | GPT-5.5 | MiniMax M3 | Result |
|---|---|---|---|
| GPQASource | 93.6% | — | Not comparable |
| GPQA-DSource | 93.6% | — | Not comparable |
| HLESource | 52.2% | — | Not comparable |
| HLE w/o toolsSource | 41.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 54.8% | 44.4% | GPT-5.5 leads |
| AA-GPQA DiamondSource | 93.5% | 92.9% | GPT-5.5 leads |
| AA-HLESource | 44.3% | 37.1% | GPT-5.5 leads |
| AA-Omniscience IndexSource | 20.1% | 1.4% | GPT-5.5 leads |
| AA-Omniscience AccuracySource | 56.9% | 15.0% | GPT-5.5 leads |
| AA-Omniscience Hallucination RateSource | 85.5% | 16.1% | MiniMax M3 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
MathMiniMax M3 wins4 benchmarks
MultimodalGPT-5.5 wins8 benchmarks
| Benchmark | GPT-5.5 | MiniMax M3 | Result |
|---|---|---|---|
| MMMU-ProSource | 81.2% | 78.1% | GPT-5.5 leads |
| MMMU-Pro w/ PythonSource | 83.2% | — | Not comparable |
| OfficeQA ProSource | 54.1% | 45.1% | GPT-5.5 leads |
| AA-MMMU-ProSource | 79.9% | 78.6% | GPT-5.5 leads |
| Design Arena WebsiteSource | 1287 | 1294 | MiniMax M3 leads |
| OmniDocBench 1.5Source | — | 91.6% | Not comparable |
| VideoMMMUSource | — | 84.6% | Not comparable |
| Video-MME (with subtitle)Source | — | 85.4% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.5 | MiniMax M3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.9% | 82.9% | MiniMax M3 leads |
Frequently Asked Questions (5)
Which is better, GPT-5.5 or MiniMax M3?
GPT-5.5 is ahead on BenchLM's provisional leaderboard, 78 to 70. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 82% and 66%.
Which is better for coding, GPT-5.5 or MiniMax M3?
MiniMax M3 has the edge for coding in this comparison, averaging 72.2 versus 58.6. Inside this category, AA Terminal-Bench 2.1 is the benchmark that creates the most daylight between them.
Which is better for math, GPT-5.5 or MiniMax M3?
MiniMax M3 has the edge for math in this comparison, averaging 85.7 versus 47.6. GPT-5.5 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GPT-5.5 or MiniMax M3?
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 72.3. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, GPT-5.5 or MiniMax M3?
GPT-5.5 has the edge for multimodal and grounded tasks in this comparison, averaging 70.4 versus 64.9. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
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