Model comparison
Claude Opus 4 vs Qwen3.8 27B
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 43.1 on the Noometry Index.
Last verified . 25 shared benchmarks.
Alibaba (Qwen)Summary
- They share 25 benchmarks with published results for both. Claude Opus 4 scores higher in 4 categories and Qwen3.8 27B in 6 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.8 27B leads 41.0 to 27.3.
- The biggest single-benchmark swing is ARC-AGI-1: 35.7% for Claude Opus 4 and 87.5% for Qwen3.8 27B.
- Qwen3.8 27B is cheaper at $0.99 / $1.49 per million input/output tokens, against $15 / $75 for Claude Opus 4.
- Qwen3.8 27B accepts more context: 262K tokens versus 200K.
- Qwen3.8 27B has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4 | Qwen3.8 27B | |
|---|---|---|
| Provider | Anthropic | Alibaba (Qwen) |
| Noometry Index | 43.1 | 46.0 |
| Released | 2025-05-22 | 2026-08-14 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 32K | 33K |
| Input $ / M tokens | $15 | $0.99 |
| Output $ / M tokens | $75 | $1.49 |
| Results tracked | 56 | 31 |
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Category by category
Coding Qwen3.8 27B leads
Claude Opus 4: 47.2 (#62), Qwen3.8 27B: 50.5 (#44)
| Benchmark | Claude Opus 4 | Qwen3.8 27B |
|---|---|---|
| LMArena Coding | 1442 | 1482 |
| SWE-bench Verified | 70.7% | — |
| SWE-bench Verified (bash only) | 67.6% | — |
| Aider Polyglot | 72% | — |
| LMArena WebDev | — | 1593 |
| SciCode | — | 46.6% |
| GSO | 6.9% | — |
| WeirdML | 43.7% | — |
| AlgoTune | 1.33 | — |
Agentic & Tool Use Claude Opus 4 leads
Claude Opus 4: 34.8 (#42), Qwen3.8 27B: 32.9 (#57)
| Benchmark | Claude Opus 4 | Qwen3.8 27B |
|---|---|---|
| APEX-Agents | — | 47.5% |
| Cybench | 38% | — |
| DeepResearch Bench | 46.8% | — |
| LMArena Search | 1127 | — |
| METR Time Horizons | 63.9% | — |
Reasoning Qwen3.8 27B leads
Claude Opus 4: 27.3 (#121), Qwen3.8 27B: 41.0 (#54)
| Benchmark | Claude Opus 4 | Qwen3.8 27B |
|---|---|---|
| ARC-AGI-2 | 8.6% | 42.4% |
| ARC-AGI-1 | 35.7% | 87.5% |
| CritPt | 0.3% | 5.4% |
| LMArena Hard Prompts | 1399 | 1460 |
| DTBench | 81.6% | 88% |
| LMCA | 37.4% | 41.4% |
| Epoch Capabilities Index | 142.67 | 149.38 |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 74.3% | — |
| NYT Connections (extended) | — | 54.5% |
| EnigmaEval | 5.6% | — |
| Surface Evolver Bench | — | 45% |
| ForecastBench | 61.1 | — |
Math Claude Opus 4 leads
Claude Opus 4: 42.0 (#86), Qwen3.8 27B: 37.1 (#161)
| Benchmark | Claude Opus 4 | Qwen3.8 27B |
|---|---|---|
| LMArena Math | 1390 | 1456 |
| OTIS Mock AIME 2024-2025 | 64.4% | — |
| ProofBench | — | 16% |
| Omni-MATH | 61.6% | — |
| MATH Level 5 | 85% | — |
| FrontierMath (Feb 2025 set) | 4.5% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Claude Opus 4 leads
Claude Opus 4: 44.0 (#88), Qwen3.8 27B: 41.6 (#109)
| Benchmark | Claude Opus 4 | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1386 | 1482 |
| GPQA Diamond | 76.3% | — |
| Humanity's Last Exam | 10.7% | — |
| MMLU-Pro | 87.5% | — |
| Confabulations | 15.9% | — |
| Vectara Hallucination Rate | 12% | — |
| GPQA (HELM) | 70.8% | — |
Multimodal Qwen3.8 27B leads
Claude Opus 4: 31.5 (#106), Qwen3.8 27B: 41.3 (#37)
| Benchmark | Claude Opus 4 | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | 1192 | 1271 |
| GeoBench | 49% | — |
| VPCT | 38% | — |
Multilingual Qwen3.8 27B leads
Claude Opus 4: 48.8 (#138), Qwen3.8 27B: 53.7 (#60)
| Benchmark | Claude Opus 4 | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1362 | 1430 |
| LMArena Chinese | 1386 | 1504 |
| LMArena French | 1372 | 1465 |
| LMArena German | 1391 | 1438 |
| LMArena Japanese | 1331 | 1384 |
| LMArena Korean | 1321 | 1393 |
| LMArena Russian | 1392 | 1415 |
| LMArena Spanish | 1389 | 1448 |
Instruction Following Claude Opus 4 leads
Claude Opus 4: 77.1 (#28), Qwen3.8 27B: 75.8 (#53)
| Benchmark | Claude Opus 4 | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1406 | 1439 |
| IFEval | 91.8% | — |
Long Context Qwen3.8 27B leads
Claude Opus 4: 39.6 (#172), Qwen3.8 27B: 44.3 (#70)
| Benchmark | Claude Opus 4 | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1422 | 1450 |
| Fiction.LiveBench | 61.1% | — |
Writing & Preference Qwen3.8 27B leads
Claude Opus 4: 61.2 (#89), Qwen3.8 27B: 65.8 (#43)
| Benchmark | Claude Opus 4 | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1377 | 1441 |
| LMArena Creative Writing | 1387 | 1384 |
| EQ-Bench Creative Writing | 1580 | 1671 |
| LMArena Multi-Turn | 1396 | 1441 |
| Short-Story Creative Writing | 83.6% | — |
| WildBench | 85.2% | — |
Frequently asked questions
Is Claude Opus 4 better than Qwen3.8 27B?
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 43.1 on the Noometry Index.
Which is cheaper, Claude Opus 4 or Qwen3.8 27B?
Qwen3.8 27B is cheaper. It lists at $0.99 per million input tokens and $1.49 per million output tokens; Claude Opus 4 lists at $15 and $75.
Is Claude Opus 4 or Qwen3.8 27B better for coding?
Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 47.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 27B does, with 262K tokens against 200K.
How many benchmarks do Claude Opus 4 and Qwen3.8 27B share?
25 benchmarks have published results for both models. Claude Opus 4 has 56 scored results on Noometry and Qwen3.8 27B has 31.