Model comparison
Gemma 4 31B 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: Gemma 4 31B unranked; MiniMax M3 #18
BenchAlign evidence: Gemma 4 31B supported; MiniMax M3 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemma 4 31B and MiniMax M3 share 21 comparable benchmark results. 2 of 8 categories are comparable. 9 results are unique to Gemma 4 31B; 24 to MiniMax M3.
Updated July 16, 2026- Shared results
- 21
- Gemma 4 31B only
- 9
- MiniMax M3 only
- 24
- Comparable categories
- 2 / 8
Pick MiniMax M3 if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if multimodal & grounded is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 21 shared benchmark results across 6 evidence categories; 2 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 62. 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 coding, where it averages 72.2 against 41.6. The single biggest benchmark swing on the page is MMMU-Pro, 76.9% to 78.1%. Gemma 4 31B does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
MiniMax M3 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 31B. That is roughly Infinityx on output cost alone. Gemma 4 31B 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 256K for Gemma 4 31B.
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 | Gemma 4 31B | Δ | MiniMax M3 |
|---|---|---|---|
| Coding | Gemma 4 31B41.6 | Margin→ 30.6 | MiniMax M372.2 |
| Multimodal | Gemma 4 31B76.9 | Margin← 12.0 | MiniMax M364.9 |
| Agentic | Gemma 4 31BNot measured | MarginNo overlap | MiniMax M372.3 |
| Knowledge | Gemma 4 31B53.3 | MarginNo overlap | MiniMax M3Not measured |
| Math | Gemma 4 31BNot measured | MarginNo overlap | MiniMax M385.7 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMMU-Pro
MultimodalA 76.9%B 78.1%Winner: MiniMax M3Δ 1.2MMMU-Pro: Gemma 4 31B scored 76.9%; 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 | Gemma 4 31B | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemma 4 31B$0 input / $0 output | MiniMax M3$0.3 input / $1.2 output | Gemma 4 31B has the lower combined listed price. |
| Generation speedtokens per second | Gemma 4 31BNot available | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemma 4 31BNot available | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemma 4 31B256K | MiniMax M31M | MiniMax M3 lists the larger context window. |
Benchmark Deep Dive
Agentic19 benchmarks
| Benchmark | Gemma 4 31B | MiniMax M3 | Result |
|---|---|---|---|
| AA Agentic IndexSource | 14.4% | 35.4% | MiniMax M3 leads |
| τ²-bench resultsSource | 59.9% | 88.9% | MiniMax M3 leads |
| GDPval-AASource | 15.2% | 44.7% | MiniMax M3 leads |
| GDPval-AASource | 804 | 1395 | MiniMax M3 leads |
| Gert LabsSource | 35.26% | — | Not comparable |
| AA EnterpriseOps-GymSource | 28.3% | 32.1% | MiniMax M3 leads |
| AA Harvey LABSource | 0.0% | 6.7% | MiniMax M3 leads |
| AA ITBenchSource | 37.3% | — | Not comparable |
| AA Tau3 BankingSource | 15.1% | — | Not comparable |
| 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 |
CodingMiniMax M3 wins13 benchmarks
| Benchmark | Gemma 4 31B | MiniMax M3 | Result |
|---|---|---|---|
| SWE-RebenchSource | 41.6% | — | Not comparable |
| React Native EvalsSource | 75.2% | — | Not comparable |
| AA Coding IndexSource | 43.4% | 58.6% | MiniMax M3 leads |
| Terminal-Bench HardSource | 36.4% | 42.4% | MiniMax M3 leads |
| AA-SciCodeSource | 43.4% | 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
Knowledge11 benchmarks
| Benchmark | Gemma 4 31B | MiniMax M3 | Result |
|---|---|---|---|
| GPQASource | 84.3% | — | Not comparable |
| MMLU-ProSource | 85.2% | — | Not comparable |
| HLESource | 26.5% | — | Not comparable |
| HLE w/o toolsSource | 19.5% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 29.4% | 44.4% | MiniMax M3 leads |
| AA-GPQA DiamondSource | 85.7% | 92.9% | MiniMax M3 leads |
| AA-HLESource | 22.7% | 37.1% | MiniMax M3 leads |
| AA-Omniscience IndexSource | -45.4% | 1.4% | MiniMax M3 leads |
| AA-Omniscience AccuracySource | 19.9% | 15.0% | Gemma 4 31B leads |
| AA-Omniscience Hallucination RateSource | 81.6% | 16.1% | MiniMax M3 leads |
| AA Openness IndexSource | 38.9% | 33.3% | Gemma 4 31B leads |
Math1 benchmarks
| Benchmark | Gemma 4 31B | MiniMax M3 | Result |
|---|---|---|---|
| USAMO 2026Source | — | 85.7% | Not comparable |
MultimodalGemma 4 31B wins7 benchmarks
| Benchmark | Gemma 4 31B | MiniMax M3 | Result |
|---|---|---|---|
| MMMU-ProSource | 76.9% | 78.1% | MiniMax M3 leads |
| AA-MMMU-ProSource | 73.4% | 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 | Gemma 4 31B | MiniMax M3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.6% | 82.9% | MiniMax M3 leads |
Frequently Asked Questions (3)
Which is better, Gemma 4 31B or MiniMax M3?
MiniMax M3 is ahead on BenchLM's provisional leaderboard, 70 to 62. The biggest single separator in this matchup is MMMU-Pro, where the scores are 76.9% and 78.1%.
Which is better for coding, Gemma 4 31B or MiniMax M3?
MiniMax M3 has the edge for coding in this comparison, averaging 72.2 versus 41.6. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, Gemma 4 31B or MiniMax M3?
Gemma 4 31B has the edge for multimodal and grounded tasks in this comparison, averaging 76.9 versus 64.9. Inside this category, AA-MMMU-Pro is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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