Model comparison
Gemma 4 31B vs Qwen3.6-27B
Head-to-head evidence from 21 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemma 4 31B #43 (Supported); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemma 4 31B and Qwen3.6-27B share 21 comparable benchmark results. 3 of 8 categories are comparable. 8 results are unique to Gemma 4 31B; 33 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 21
- Gemma 4 31B only
- 8
- Qwen3.6-27B only
- 33
- Comparable categories
- 3 / 8
Pick Gemma 4 31B if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if coding is the priority or you need the larger 262K context window.
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
Gemma 4 31B is clearly ahead on the BenchAlign aggregate, 61.08 to 53.82. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemma 4 31B's sharpest advantage is in multimodal & grounded, where it averages 76.9 against 76.7. The single biggest benchmark swing on the page is GPQA, 84.3% to 87.8%. Qwen3.6-27B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Qwen3.6-27B gives you the larger context window at 262K, 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 | Qwen3.6-27B |
|---|---|---|---|
| Coding | Gemma 4 31B41.6 | Margin→ 35.9 | Qwen3.6-27B77.5 |
| Knowledge | Gemma 4 31B52.9 | Margin→ 0.4 | Qwen3.6-27B53.3 |
| Multimodal | Gemma 4 31B76.9 | Margin← 0.2 | Qwen3.6-27B76.7 |
| Agentic | Gemma 4 31BNot measured | MarginNo overlap | Qwen3.6-27B59.3 |
| Math | Gemma 4 31BNot measured | MarginNo overlap | Qwen3.6-27B89.2 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 84.3%B 87.8%Winner: Qwen3.6-27BΔ 3.5GPQA: Gemma 4 31B scored 84.3%; Qwen3.6-27B scored 87.8%. Qwen3.6-27B wins this benchmark. - Source ↗
HLE
KnowledgeA 26.5%B 24%Winner: Gemma 4 31BΔ 2.5HLE: Gemma 4 31B scored 26.5%; Qwen3.6-27B scored 24%. Gemma 4 31B wins this benchmark. - Source ↗
MMMU-Pro
MultimodalA 76.9%B 75.8%Winner: Gemma 4 31BΔ 1.1MMMU-Pro: Gemma 4 31B scored 76.9%; Qwen3.6-27B scored 75.8%. Gemma 4 31B wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 85.2%B 86.2%Winner: Qwen3.6-27BΔ 1MMLU-Pro: Gemma 4 31B scored 85.2%; Qwen3.6-27B scored 86.2%. Qwen3.6-27B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemma 4 31B | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemma 4 31B$0 input / $0 output | Qwen3.6-27B$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | Gemma 4 31BNot available | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemma 4 31BNot available | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemma 4 31B256K | Qwen3.6-27B262K | Qwen3.6-27B lists the larger context window. |
Benchmark Deep Dive
Agentic14 benchmarks
| Benchmark | Gemma 4 31B | Qwen3.6-27B | Result |
|---|---|---|---|
| AA Agentic IndexSource | 14.4% | 27.0% | Qwen3.6-27B leads |
| τ²-bench resultsSource | 59.9% | 94.2% | Qwen3.6-27B leads |
| GDPval-AASource | 15.2% | 32.0% | Qwen3.6-27B leads |
| GDPval-AASource | 804 | 1140 | Qwen3.6-27B leads |
| Gert LabsSource | 35.26% | 54.84% | Qwen3.6-27B leads |
| AA EnterpriseOps-GymSource | 28.3% | — | Not comparable |
| AA ITBenchSource | 37.3% | — | Not comparable |
| AA Tau3 BankingSource | 15.1% | — | Not comparable |
| terminalBenchHardSource | 36.4% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| Claw-EvalSource | — | 72.4% | Not comparable |
| QwenClawBenchSource | — | 53.4% | Not comparable |
| QwenWebBenchSource | — | 1487 | Not comparable |
| AndroidWorldSource | — | 70.3% | Not comparable |
CodingQwen3.6-27B wins10 benchmarks
| Benchmark | Gemma 4 31B | Qwen3.6-27B | Result |
|---|---|---|---|
| SWE-RebenchSource | 41.6% | — | Not comparable |
| React Native EvalsSource | 75.2% | — | Not comparable |
| AA Coding IndexSource | 43.4% | 53.7% | Qwen3.6-27B leads |
| AA-SciCodeSource | 43.4% | 39.8% | Gemma 4 31B leads |
| SWE-bench VerifiedSource | — | 77.2% | Not comparable |
| SWE MultilingualSource | — | 71.3% | Not comparable |
| SWE-bench ProSource | — | 53.5% | Not comparable |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| LiveCodeBenchSource | — | 83.9% | Not comparable |
| NL2RepoSource | — | 36.2% | Not comparable |
Reasoning2 benchmarks
KnowledgeQwen3.6-27B wins14 benchmarks
| Benchmark | Gemma 4 31B | Qwen3.6-27B | Result |
|---|---|---|---|
| GPQASource | 84.3% | 87.8% | Qwen3.6-27B leads |
| MMLU-ProSource | 85.2% | 86.2% | Qwen3.6-27B leads |
| HLESource | 26.5% | 24% | Gemma 4 31B leads |
| HLE w/o toolsSource | 19.5% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 29.4% | 37.0% | Qwen3.6-27B leads |
| AA-GPQA DiamondSource | 85.7% | 84.2% | Gemma 4 31B leads |
| AA-HLESource | 22.7% | 21.6% | Gemma 4 31B leads |
| AA-Omniscience IndexSource | -45.4% | -19.8% | Qwen3.6-27B leads |
| AA-Omniscience AccuracySource | 19.9% | 19.2% | Gemma 4 31B leads |
| AA-Omniscience Hallucination RateSource | 81.6% | 48.3% | Qwen3.6-27B leads |
| AA Openness IndexSource | 38.9% | — | Not comparable |
| MMLU-ReduxSource | — | 93.5% | Not comparable |
| SuperGPQASource | — | 66% | Not comparable |
| C-EvalSource | — | 91.4% | Not comparable |
Math5 benchmarks
MultimodalGemma 4 31B wins16 benchmarks
| Benchmark | Gemma 4 31B | Qwen3.6-27B | Result |
|---|---|---|---|
| MMMU-ProSource | 76.9% | 75.8% | Gemma 4 31B leads |
| AA-MMMU-ProSource | 73.4% | 74.6% | Qwen3.6-27B leads |
| MMMUSource | — | 82.9% | Not comparable |
| RealWorldQASource | — | 84.1% | Not comparable |
| DynaMathSource | — | 85.6% | Not comparable |
| MStarSource | — | 81.4% | Not comparable |
| SimpleVQASource | — | 56.1% | Not comparable |
| CharXivSource | — | 78.4% | Not comparable |
| CC-OCRSource | — | 81.2% | Not comparable |
| CountBenchSource | — | 97.8% | Not comparable |
| RefCOCO (avg)Source | — | 92.5% | Not comparable |
| ERQASource | — | 62.5% | Not comparable |
| Video-MME (with subtitle)Source | — | 87.7% | Not comparable |
| VideoMMMUSource | — | 84.4% | Not comparable |
| MLVU (M-Avg)Source | — | 86.6% | Not comparable |
| V*Source | — | 94.7% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Gemma 4 31B | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.6% | 67.6% | Gemma 4 31B leads |
Frequently Asked Questions (4)
Which is better, Gemma 4 31B or Qwen3.6-27B?
Gemma 4 31B is ahead on BenchLM's BenchAlign leaderboard, 61.08 to 53.82. The biggest single separator in this matchup is GPQA, where the scores are 84.3% and 87.8%.
Which is better for knowledge tasks, Gemma 4 31B or Qwen3.6-27B?
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 53.3 versus 52.9. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, Gemma 4 31B or Qwen3.6-27B?
Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 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 Qwen3.6-27B?
Gemma 4 31B has the edge for multimodal and grounded tasks in this comparison, averaging 76.9 versus 76.7. 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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