Model comparison
Gemma 4 12B vs MiniMax M3
Head-to-head evidence from 13 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemma 4 12B #137 (Estimated); MiniMax M3 #15 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemma 4 12B and MiniMax M3 share 13 comparable benchmark results. 2 of 8 categories are comparable. 10 results are unique to Gemma 4 12B; 32 to MiniMax M3.
Updated July 23, 2026- Shared results
- 13
- Gemma 4 12B only
- 10
- MiniMax M3 only
- 32
- Comparable categories
- 2 / 8
Pick MiniMax M3 if you want the stronger benchmark profile. Gemma 4 12B 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 13 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 BenchAlign aggregate, 69.75 to 47.29. 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 77.5. The single biggest benchmark swing on the page is MMMU-Pro, 69.1% to 78.1%. Gemma 4 12B does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
Gemma 4 12B 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 12B.
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 12B | Δ | MiniMax M3 |
|---|---|---|---|
| Math | Gemma 4 12B77.5 | Margin→ 8.2 | MiniMax M385.7 |
| Multimodal | Gemma 4 12B69.1 | Margin← 4.2 | MiniMax M364.9 |
| Agentic | Gemma 4 12BNot measured | MarginNo overlap | MiniMax M372.3 |
| Coding | Gemma 4 12BNot measured | MarginNo overlap | MiniMax M372.2 |
| Reasoning | Gemma 4 12B43.4 | MarginNo overlap | MiniMax M3Not measured |
| Knowledge | Gemma 4 12B77.5 | MarginNo overlap | MiniMax M3Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMMU-Pro
MultimodalA 69.1%B 78.1%Winner: MiniMax M3Δ 9MMMU-Pro: Gemma 4 12B scored 69.1%; 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 | Gemma 4 12B | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemma 4 12BNot available | MiniMax M3$0.3 input / $1.2 output | A complete price comparison is not available. |
| Generation speedtokens per second | Gemma 4 12BNot available | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemma 4 12BNot available | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemma 4 12B256K | MiniMax M31M | MiniMax M3 lists the larger context window. |
Benchmark Deep Dive
Agentic18 benchmarks
| Benchmark | Gemma 4 12B | MiniMax M3 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 36.3% | 88.9% | 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 |
| AA Agentic IndexSource | — | 35.4% | Not comparable |
| GDPval-AASource | — | 44.7% | Not comparable |
| GDPval-AASource | — | 1395 | 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 | — | 88.4% | Not comparable |
| terminalBenchHardSource | — | 42.4% | Not comparable |
| aaTerminalBench21Source | — | 65.2% | Not comparable |
Coding9 benchmarks
| Benchmark | Gemma 4 12B | MiniMax M3 | Result |
|---|---|---|---|
| AA-SciCodeSource | 38.2% | 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 |
| AA Coding IndexSource | — | 58.6% | Not comparable |
| VIBE V2Source | — | 50.1% | Not comparable |
| SVG-BenchSource | — | 63.7% | Not comparable |
| KernelBench HardSource | — | 28.8% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | Gemma 4 12B | MiniMax M3 | Result |
|---|---|---|---|
| GPQASource | 78.8% | — | Not comparable |
| GPQA-DSource | 78.8% | — | Not comparable |
| MMLU-ProSource | 77.2% | — | Not comparable |
| HLE w/o toolsSource | 5.2% | — | Not comparable |
| MMMLUSource | 83.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 22.0% | 44.4% | MiniMax M3 leads |
| AA-GPQA DiamondSource | 75.3% | 92.9% | MiniMax M3 leads |
| AA-HLESource | 14.8% | 37.1% | MiniMax M3 leads |
| AA-Omniscience IndexSource | -51.9% | 1.4% | MiniMax M3 leads |
| AA-Omniscience AccuracySource | 16.0% | 15.0% | Gemma 4 12B leads |
| AA-Omniscience Hallucination RateSource | 80.8% | 16.1% | MiniMax M3 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
MathMiniMax M3 wins2 benchmarks
MultimodalGemma 4 12B wins9 benchmarks
| Benchmark | Gemma 4 12B | MiniMax M3 | Result |
|---|---|---|---|
| MMMU-ProSource | 69.1% | 78.1% | MiniMax M3 leads |
| MathVisionSource | 79.7% | — | Not comparable |
| MedXpertQA (MM)Source | 48.7% | — | Not comparable |
| AA-MMMU-ProSource | 69.7% | 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 | — | 1289 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Gemma 4 12B | MiniMax M3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.5% | 82.9% | MiniMax M3 leads |
Frequently Asked Questions (3)
Which is better, Gemma 4 12B or MiniMax M3?
MiniMax M3 is ahead on BenchLM's BenchAlign leaderboard, 69.75 to 47.29. The biggest single separator in this matchup is MMMU-Pro, where the scores are 69.1% and 78.1%.
Which is better for math, Gemma 4 12B or MiniMax M3?
MiniMax M3 has the edge for math in this comparison, averaging 85.7 versus 77.5. Gemma 4 12B stays close enough that the answer can still flip depending on your workload.
Which is better for multimodal and grounded tasks, Gemma 4 12B or MiniMax M3?
Gemma 4 12B has the edge for multimodal and grounded tasks in this comparison, averaging 69.1 versus 64.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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