Model comparison
Gemma 4 31B vs GPT-5.5
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: Gemma 4 31B #43 (Supported); GPT-5.5 #9 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemma 4 31B and GPT-5.5 share 26 comparable benchmark results. 3 of 8 categories are comparable. 3 results are unique to Gemma 4 31B; 31 to GPT-5.5.
Updated July 22, 2026- Shared results
- 26
- Gemma 4 31B only
- 3
- GPT-5.5 only
- 31
- Comparable categories
- 3 / 8
Pick GPT-5.5 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 26 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
GPT-5.5 is clearly ahead on the BenchAlign aggregate, 73.51 to 61.08. 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 coding, where it averages 58.6 against 41.6. The single biggest benchmark swing on the page is HLE, 26.5% to 52.2%. 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.
GPT-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 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. GPT-5.5 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 | GPT-5.5 |
|---|---|---|---|
| Coding | Gemma 4 31B41.6 | Margin→ 17.0 | GPT-5.558.6 |
| Multimodal | Gemma 4 31B76.9 | Margin← 6.5 | GPT-5.570.4 |
| Knowledge | Gemma 4 31B52.9 | Margin→ 4.9 | GPT-5.557.8 |
| Agentic | Gemma 4 31BNot measured | MarginNo overlap | GPT-5.581.6 |
| Reasoning | Gemma 4 31BNot measured | MarginNo overlap | GPT-5.585.0 |
| Math | Gemma 4 31BNot measured | MarginNo overlap | GPT-5.547.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 26.5%B 52.2%Winner: GPT-5.5Δ 25.7HLE: Gemma 4 31B scored 26.5%; GPT-5.5 scored 52.2%. GPT-5.5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 84.3%B 93.6%Winner: GPT-5.5Δ 9.3GPQA: Gemma 4 31B scored 84.3%; GPT-5.5 scored 93.6%. GPT-5.5 wins this benchmark. - Source ↗
MMMU-Pro
MultimodalA 76.9%B 81.2%Winner: GPT-5.5Δ 4.3MMMU-Pro: Gemma 4 31B scored 76.9%; GPT-5.5 scored 81.2%. GPT-5.5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemma 4 31B | GPT-5.5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemma 4 31B$0 input / $0 output | GPT-5.5$5 input / $30 output | Gemma 4 31B has the lower combined listed price. |
| Generation speedtokens per second | Gemma 4 31BNot available | GPT-5.5Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemma 4 31BNot available | GPT-5.5Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemma 4 31B256K | GPT-5.51M | GPT-5.5 lists the larger context window. |
Benchmark Deep Dive
Agentic24 benchmarks
| Benchmark | Gemma 4 31B | GPT-5.5 | Result |
|---|---|---|---|
| AA Agentic IndexSource | 14.4% | 44.9% | GPT-5.5 leads |
| τ²-bench resultsSource | 59.9% | 93.9% | GPT-5.5 leads |
| GDPval-AASource | 15.2% | 49.5% | GPT-5.5 leads |
| GDPval-AASource | 804 | 1490 | GPT-5.5 leads |
| Gert LabsSource | 35.26% | 72.93% | GPT-5.5 leads |
| AA EnterpriseOps-GymSource | 28.3% | 46.6% | GPT-5.5 leads |
| AA ITBenchSource | 37.3% | 45.8% | GPT-5.5 leads |
| AA Tau3 BankingSource | 15.1% | 31.3% | GPT-5.5 leads |
| terminalBenchHardSource | 36.4% | 60.6% | GPT-5.5 leads |
| Terminal-Bench 2.0Source | — | 82% | Not comparable |
| CyberGymSource | — | 81.8% | Not comparable |
| BrowseCompSource | — | 84.4% | Not comparable |
| OSWorld-VerifiedSource | — | 78.7% | Not comparable |
| MCP AtlasSource | — | 75.3% | Not comparable |
| ToolathlonSource | — | 55.6% | Not comparable |
| APEX-Agents-AASource | — | 37.7% | Not comparable |
| ResearchClawBenchSource | — | 17.0% | Not comparable |
| OSWorld 2.0Source | — | 13.0% | Not comparable |
| JobBenchSource | — | 42.7% | Not comparable |
| ExploitGymSource | — | 13.4% | Not comparable |
| AA BriefcaseSource | — | 1154 | Not comparable |
| AA AutomationBenchSource | — | 42.1% | Not comparable |
| AA Harvey LABSource | — | 86.3% | Not comparable |
| aaTerminalBench21Source | — | 84.3% | Not comparable |
CodingGPT-5.5 wins10 benchmarks
| Benchmark | Gemma 4 31B | GPT-5.5 | Result |
|---|---|---|---|
| SWE-RebenchSource | 41.6% | — | Not comparable |
| React Native EvalsSource | 75.2% | 84.7% | GPT-5.5 leads |
| AA Coding IndexSource | 43.4% | 74.9% | GPT-5.5 leads |
| AA-SciCodeSource | 43.4% | 56.1% | GPT-5.5 leads |
| SWE-bench ProSource | — | 58.6% | Not comparable |
| Terminal-Bench 2.0Source | — | 82.0% | Not comparable |
| Vibe Code BenchSource | — | 69.85% | Not comparable |
| cursorBench31Source | — | 59.2% | Not comparable |
| cursorBench32Source | — | 58.4% | Not comparable |
| FrontierCode 1.1 MainSource | — | 43.0% | Not comparable |
Reasoning5 benchmarks
KnowledgeGPT-5.5 wins12 benchmarks
| Benchmark | Gemma 4 31B | GPT-5.5 | Result |
|---|---|---|---|
| GPQASource | 84.3% | 93.6% | GPT-5.5 leads |
| MMLU-ProSource | 85.2% | — | Not comparable |
| HLESource | 26.5% | 52.2% | GPT-5.5 leads |
| HLE w/o toolsSource | 19.5% | 41.4% | GPT-5.5 leads |
| Artificial Analysis Intelligence IndexSource | 29.4% | 54.8% | GPT-5.5 leads |
| AA-GPQA DiamondSource | 85.7% | 93.5% | GPT-5.5 leads |
| AA-HLESource | 22.7% | 44.3% | GPT-5.5 leads |
| AA-Omniscience IndexSource | -45.4% | 20.1% | GPT-5.5 leads |
| AA-Omniscience AccuracySource | 19.9% | 56.9% | GPT-5.5 leads |
| AA-Omniscience Hallucination RateSource | 81.6% | 85.5% | Gemma 4 31B leads |
| AA Openness IndexSource | 38.9% | — | Not comparable |
| GPQA-DSource | — | 93.6% | Not comparable |
Math3 benchmarks
MultimodalGemma 4 31B wins5 benchmarks
Inst. Following1 benchmarks
| Benchmark | Gemma 4 31B | GPT-5.5 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.6% | 75.9% | GPT-5.5 leads |
Frequently Asked Questions (4)
Which is better, Gemma 4 31B or GPT-5.5?
GPT-5.5 is ahead on BenchLM's BenchAlign leaderboard, 73.51 to 61.08. The biggest single separator in this matchup is HLE, where the scores are 26.5% and 52.2%.
Which is better for knowledge tasks, Gemma 4 31B or GPT-5.5?
GPT-5.5 has the edge for knowledge tasks in this comparison, averaging 57.8 versus 52.9. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, Gemma 4 31B or GPT-5.5?
GPT-5.5 has the edge for coding in this comparison, averaging 58.6 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 GPT-5.5?
Gemma 4 31B has the edge for multimodal and grounded tasks in this comparison, averaging 76.9 versus 70.4. 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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