Model comparison
GPT-5.6 Sol vs MiniMax M3
Head-to-head evidence from 26 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.6 Sol #3 (Supported); MiniMax M3 #15 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.6 Sol and MiniMax M3 share 26 comparable benchmark results. 4 of 8 categories are comparable. 20 results are unique to GPT-5.6 Sol; 19 to MiniMax M3.
Updated July 23, 2026- Shared results
- 26
- GPT-5.6 Sol only
- 20
- MiniMax M3 only
- 19
- Comparable categories
- 4 / 8
Pick GPT-5.6 Sol if you want the stronger benchmark profile. MiniMax M3 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 26 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-5.6 Sol is clearly ahead on the BenchAlign aggregate, 81.96 to 69.75. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.6 Sol's sharpest advantage is in agentic, where it averages 92 against 72.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 91.9% to 66%. MiniMax M3 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-5.6 Sol 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-5.6 Sol 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-5.6 Sol | Δ | MiniMax M3 |
|---|---|---|---|
| Agentic | GPT-5.6 Sol92.0 | Margin← 19.7 | MiniMax M372.3 |
| Multimodal | GPT-5.6 Sol83.0 | Margin← 18.1 | MiniMax M364.9 |
| Coding | GPT-5.6 Sol64.6 | Margin→ 7.6 | MiniMax M372.2 |
| Math | GPT-5.6 Sol87.5 | Margin← 1.8 | MiniMax M385.7 |
| Knowledge | GPT-5.6 Sol94.6 | 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 91.9%B 66%Winner: GPT-5.6 SolΔ 25.9Terminal-Bench 2.0: GPT-5.6 Sol scored 91.9%; MiniMax M3 scored 66%. GPT-5.6 Sol wins this benchmark. - Source ↗
BrowseComp
AgenticA 92.2%B 83.5%Winner: GPT-5.6 SolΔ 8.7BrowseComp: GPT-5.6 Sol scored 92.2%; MiniMax M3 scored 83.5%. GPT-5.6 Sol wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 64.6%B 59%Winner: GPT-5.6 SolΔ 5.6SWE-bench Pro: GPT-5.6 Sol scored 64.6%; MiniMax M3 scored 59%. GPT-5.6 Sol wins this benchmark. - Source ↗
MMMU-Pro
MultimodalA 83%B 78.1%Winner: GPT-5.6 SolΔ 4.9MMMU-Pro: GPT-5.6 Sol scored 83%; MiniMax M3 scored 78.1%. GPT-5.6 Sol wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.6 Sol | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.6 Sol$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-5.6 SolNot available | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.6 SolNot available | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.6 Sol1M | MiniMax M31M | Listed context windows are equal. |
Benchmark Deep Dive
AgenticGPT-5.6 Sol wins24 benchmarks
| Benchmark | GPT-5.6 Sol | MiniMax M3 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 91.9% | 66% | GPT-5.6 Sol leads |
| BrowseCompSource | 92.2% | 83.5% | GPT-5.6 Sol leads |
| OSWorld 2.0Source | 62.6% | 4.6% | GPT-5.6 Sol leads |
| CyberGymSource | 84.5% | — | Not comparable |
| ExploitGymSource | 33.7% | — | Not comparable |
| ToolathlonSource | 58% | — | Not comparable |
| AA Agentic IndexSource | 54.0% | 35.4% | GPT-5.6 Sol leads |
| τ²-bench resultsSource | 85.1% | 88.9% | MiniMax M3 leads |
| GDPval-AASource | 61.8% | 44.7% | GPT-5.6 Sol leads |
| GDPval-AASource | 1736 | 1395 | GPT-5.6 Sol leads |
| AA BriefcaseSource | 1501 | 1110 | GPT-5.6 Sol leads |
| AA ITBenchSource | 56.2% | — | Not comparable |
| AA Tau3 BankingSource | 33.0% | — | Not comparable |
| AA AutomationBenchSource | 51.2% | — | Not comparable |
| AA Harvey LABSource | 87.2% | 88.4% | MiniMax M3 leads |
| terminalBenchHardSource | 65.9% | 42.4% | GPT-5.6 Sol leads |
| aaTerminalBench21Source | 88% | 65.2% | GPT-5.6 Sol leads |
| 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 |
| AA EnterpriseOps-GymSource | — | 32.1% | Not comparable |
CodingMiniMax M3 wins13 benchmarks
| Benchmark | GPT-5.6 Sol | MiniMax M3 | Result |
|---|---|---|---|
| SWE-bench ProSource | 64.6% | 59% | GPT-5.6 Sol leads |
| Terminal-Bench 2.0Source | 91.9% | 66.0% | GPT-5.6 Sol leads |
| deepSweSource | 72.7% | — | Not comparable |
| FrontierCode 1.1 ExtendedSource | 60.6% | — | Not comparable |
| cursorBench32Source | 67.2% | — | Not comparable |
| VulcanBench v3Source | 87.0% | — | Not comparable |
| AA Coding IndexSource | 77.4% | 58.6% | GPT-5.6 Sol leads |
| AA-SciCodeSource | 56.1% | 45.4% | GPT-5.6 Sol 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 |
Reasoning4 benchmarks
Knowledge11 benchmarks
| Benchmark | GPT-5.6 Sol | MiniMax M3 | Result |
|---|---|---|---|
| GPQASource | 94.6% | — | Not comparable |
| GPQA-DSource | 94.6% | — | Not comparable |
| HealthBench ProfessionalSource | 60.5% | — | Not comparable |
| HealthBench HardSource | 33.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 58.9% | 44.4% | GPT-5.6 Sol leads |
| AA-GPQA DiamondSource | 94.1% | 92.9% | GPT-5.6 Sol leads |
| AA-HLESource | 47.2% | 37.1% | GPT-5.6 Sol leads |
| AA-Omniscience IndexSource | 21.7% | 1.4% | GPT-5.6 Sol leads |
| AA-Omniscience AccuracySource | 58.5% | 15.0% | GPT-5.6 Sol leads |
| AA-Omniscience Hallucination RateSource | 88.8% | 16.1% | MiniMax M3 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
MathGPT-5.6 Sol wins4 benchmarks
MultimodalGPT-5.6 Sol wins8 benchmarks
| Benchmark | GPT-5.6 Sol | MiniMax M3 | Result |
|---|---|---|---|
| MMMU-ProSource | 83% | 78.1% | GPT-5.6 Sol leads |
| MMMU-Pro w/ PythonSource | 84.6% | — | Not comparable |
| AA-MMMU-ProSource | 83.4% | 78.6% | GPT-5.6 Sol 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 | — | 1289 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.6 Sol | MiniMax M3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 72.7% | 82.9% | MiniMax M3 leads |
Frequently Asked Questions (5)
Which is better, GPT-5.6 Sol or MiniMax M3?
GPT-5.6 Sol is ahead on BenchLM's BenchAlign leaderboard, 81.96 to 69.75. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 91.9% and 66%.
Which is better for coding, GPT-5.6 Sol or MiniMax M3?
MiniMax M3 has the edge for coding in this comparison, averaging 72.2 versus 64.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for math, GPT-5.6 Sol or MiniMax M3?
GPT-5.6 Sol has the edge for math in this comparison, averaging 87.5 versus 85.7. MiniMax M3 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GPT-5.6 Sol or MiniMax M3?
GPT-5.6 Sol has the edge for agentic tasks in this comparison, averaging 92 versus 72.3. Inside this category, AA Briefcase is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, GPT-5.6 Sol or MiniMax M3?
GPT-5.6 Sol has the edge for multimodal and grounded tasks in this comparison, averaging 83 versus 64.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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