Model comparison
Gemma 2 27B vs Qwen3 235B-A22B
Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 29.4 on the Noometry Index. Gemma 2 27B costs 1.9× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
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Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. Gemma 2 27B scores higher in 0 categories and Qwen3 235B-A22B in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3 235B-A22B leads 50.4 to 10.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 1.4% for Gemma 2 27B and 86.7% for Qwen3 235B-A22B.
- Gemma 2 27B is cheaper at $0.65 / $0.65 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
- Qwen3 235B-A22B accepts more context: 131K tokens versus 8K.
Side by side
| Gemma 2 27B | Qwen3 235B-A22B | |
|---|---|---|
| Provider | Alibaba (Qwen) | |
| Noometry Index | 29.4 | 43.5 |
| Released | 2024-06-24 | 2025-04 |
| Weights | Open | Open |
| Context window | 8K | 131K |
| Max output | 2K | 16K |
| Input $ / M tokens | $0.65 | $0.70 |
| Output $ / M tokens | $0.65 | $2.80 |
| Results tracked | 34 | 49 |
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Category by category
Coding Qwen3 235B-A22B leads
Gemma 2 27B: 34.1 (#246), Qwen3 235B-A22B: 44.3 (#75)
| Benchmark | Gemma 2 27B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Coding | 1211 | 1445 |
| Aider Polyglot | — | 59.6% |
| SciCode | — | 42.4% |
| WeirdML | — | 41% |
| BigCodeBench Instruct | 42.8% | — |
| LiveBench Coding | 36% | — |
| BigCodeBench Complete | 52.5% | — |
Agentic & Tool Use Not comparable
Gemma 2 27B: —, Qwen3 235B-A22B: 33.9 (#51)
| Benchmark | Gemma 2 27B | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning Too close to call
Gemma 2 27B: 15.3 (#315), Qwen3 235B-A22B: 15.7 (#311)
| Benchmark | Gemma 2 27B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Hard Prompts | 1198 | 1433 |
| DTBench | 48% | 80.3% |
| LMCA | 7.1% | 29.3% |
| Epoch Capabilities Index | 122.08 | 143.85 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 11% |
| CritPt | — | 0% |
| Chess Puzzles | — | 12% |
| LiveBench Reasoning | 28.1% | — |
| Mystery Game Puzzles | — | 9% |
| LiveBench Data Analysis | 47.9% | — |
| ForecastBench | — | 59.7 |
| LiveBench | 38.2% | — |
Math Qwen3 235B-A22B leads
Gemma 2 27B: 10.7 (#311), Qwen3 235B-A22B: 50.4 (#57)
| Benchmark | Gemma 2 27B | Qwen3 235B-A22B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.4% | 86.7% |
| LMArena Math | 1212 | 1432 |
| MATH Level 5 | 27.9% | 68.9% |
| Omni-MATH | — | 71.8% |
| LiveBench Math | 26.5% | — |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3 235B-A22B leads
Gemma 2 27B: 19.0 (#280), Qwen3 235B-A22B: 49.6 (#73)
| Benchmark | Gemma 2 27B | Qwen3 235B-A22B |
|---|---|---|
| GPQA Diamond | 36.5% | 80.1% |
| Confabulations | 27.1% | 15.6% |
| LMArena Expert | 1172 | 1463 |
| SimpleQA Verified | — | 40.4% |
| MMLU-Pro | — | 84.4% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
| MMLU | 75.7% | — |
Multilingual Qwen3 235B-A22B leads
Gemma 2 27B: 38.6 (#226), Qwen3 235B-A22B: 52.3 (#89)
| Benchmark | Gemma 2 27B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1217 | 1409 |
| LMArena Chinese | 1221 | 1481 |
| LMArena French | 1247 | 1445 |
| LMArena German | 1209 | 1433 |
| LMArena Japanese | 1175 | 1399 |
| LMArena Korean | 1174 | 1391 |
| LMArena Russian | 1234 | 1411 |
| LMArena Spanish | 1228 | 1430 |
Instruction Following Qwen3 235B-A22B leads
Gemma 2 27B: 60.5 (#249), Qwen3 235B-A22B: 72.6 (#136)
| Benchmark | Gemma 2 27B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1206 | 1408 |
| LiveBench Instruction Following | 58.1% | — |
| IFEval | — | 83.5% |
Long Context Qwen3 235B-A22B leads
Gemma 2 27B: 37.3 (#218), Qwen3 235B-A22B: 46.1 (#26)
| Benchmark | Gemma 2 27B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1231 | 1426 |
| Fiction.LiveBench | — | 75% |
Writing & Preference Qwen3 235B-A22B leads
Gemma 2 27B: 44.2 (#225), Qwen3 235B-A22B: 59.6 (#108)
| Benchmark | Gemma 2 27B | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1231 | 1419 |
| LMArena Creative Writing | 1241 | 1384 |
| LMArena Multi-Turn | 1224 | 1432 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1366 |
| WildBench | — | 86.6% |
| LiveBench Language | 32.6% | — |
Frequently asked questions
Is Gemma 2 27B better than Qwen3 235B-A22B?
Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 29.4 on the Noometry Index. Gemma 2 27B costs 1.9× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Which is cheaper, Gemma 2 27B or Qwen3 235B-A22B?
Gemma 2 27B is cheaper. It lists at $0.65 per million input tokens and $0.65 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.
Is Gemma 2 27B or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 44.3 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
Qwen3 235B-A22B does, with 131K tokens against 8K.
How many benchmarks do Gemma 2 27B and Qwen3 235B-A22B share?
24 benchmarks have published results for both models. Gemma 2 27B has 34 scored results on Noometry and Qwen3 235B-A22B has 49.