Model comparison
Claude 4 Sonnet vs Qwen3.6-27B
Head-to-head evidence from 14 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude 4 Sonnet #158 (Supported); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude 4 Sonnet and Qwen3.6-27B share 14 comparable benchmark results. 1 of 8 categories are comparable. 2 results are unique to Claude 4 Sonnet; 40 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 14
- Claude 4 Sonnet only
- 2
- Qwen3.6-27B only
- 40
- Comparable categories
- 1 / 8
Pick Qwen3.6-27B if you want the stronger benchmark profile. Claude 4 Sonnet only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 6 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Qwen3.6-27B is clearly ahead on the BenchAlign aggregate, 53.82 to 42.79. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-27B's sharpest advantage is in coding, where it averages 77.5 against 72.7. The single biggest benchmark swing on the page is SWE-bench Verified, 72.7% to 77.2%.
Claude 4 Sonnet is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. That is roughly Infinityx on output cost alone. Qwen3.6-27B is the reasoning model in the pair, while Claude 4 Sonnet 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. Qwen3.6-27B gives you the larger context window at 262K, compared with 200K for Claude 4 Sonnet.
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 | Claude 4 Sonnet | Δ | Qwen3.6-27B |
|---|---|---|---|
| Coding | Claude 4 Sonnet72.7 | Margin→ 4.8 | Qwen3.6-27B77.5 |
| Agentic | Claude 4 SonnetNot measured | MarginNo overlap | Qwen3.6-27B59.3 |
| Knowledge | Claude 4 SonnetNot measured | MarginNo overlap | Qwen3.6-27B53.3 |
| Math | Claude 4 SonnetNot measured | MarginNo overlap | Qwen3.6-27B89.2 |
| Multimodal | Claude 4 SonnetNot measured | MarginNo overlap | Qwen3.6-27B76.7 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 72.7%B 77.2%Winner: Qwen3.6-27BΔ 4.5SWE-bench Verified: Claude 4 Sonnet scored 72.7%; Qwen3.6-27B scored 77.2%. Qwen3.6-27B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude 4 Sonnet | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude 4 Sonnet$3 input / $15 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | Claude 4 Sonnet40 tok/s | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude 4 Sonnet1.33 s | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude 4 Sonnet200K | Qwen3.6-27B262K | Qwen3.6-27B lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | Claude 4 Sonnet | Qwen3.6-27B | Result |
|---|---|---|---|
| τ²-bench resultsSource | 52.3% | 94.2% | Qwen3.6-27B leads |
| Gert LabsSource | 39.66% | 54.84% | Qwen3.6-27B leads |
| JobBenchSource | 18.4% | — | 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 |
| AA Agentic IndexSource | — | 27.0% | Not comparable |
| GDPval-AASource | — | 32.0% | Not comparable |
| GDPval-AASource | — | 1140 | Not comparable |
CodingQwen3.6-27B wins8 benchmarks
| Benchmark | Claude 4 Sonnet | Qwen3.6-27B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 72.7% | 77.2% | Qwen3.6-27B leads |
| AA-SciCodeSource | 37.3% | 39.8% | Qwen3.6-27B leads |
| 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 |
| AA Coding IndexSource | — | 53.7% | Not comparable |
Reasoning2 benchmarks
Knowledge12 benchmarks
| Benchmark | Claude 4 Sonnet | Qwen3.6-27B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 25.5% | 37.0% | Qwen3.6-27B leads |
| AA-GPQA DiamondSource | 68.3% | 84.2% | Qwen3.6-27B leads |
| AA-HLESource | 4.0% | 21.6% | Qwen3.6-27B leads |
| AA-Omniscience IndexSource | -9.2% | -19.8% | Claude 4 Sonnet leads |
| AA-Omniscience AccuracySource | 22.4% | 19.2% | Claude 4 Sonnet leads |
| AA-Omniscience Hallucination RateSource | 40.8% | 48.3% | Claude 4 Sonnet leads |
| MMLU-ProSource | — | 86.2% | Not comparable |
| MMLU-ReduxSource | — | 93.5% | Not comparable |
| SuperGPQASource | — | 66% | Not comparable |
| C-EvalSource | — | 91.4% | Not comparable |
| GPQASource | — | 87.8% | Not comparable |
| HLESource | — | 24% | Not comparable |
Math5 benchmarks
Multimodal17 benchmarks
| Benchmark | Claude 4 Sonnet | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 62.4% | 74.6% | Qwen3.6-27B leads |
| Design Arena WebsiteSource | 1175 | — | Not comparable |
| MMMUSource | — | 82.9% | Not comparable |
| MMMU-ProSource | — | 75.8% | 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 | Claude 4 Sonnet | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | 45.4% | 67.6% | Qwen3.6-27B leads |
Frequently Asked Questions (2)
Which is better, Claude 4 Sonnet or Qwen3.6-27B?
Qwen3.6-27B is ahead on BenchLM's BenchAlign leaderboard, 53.82 to 42.79. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 72.7% and 77.2%.
Which is better for coding, Claude 4 Sonnet or Qwen3.6-27B?
Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 versus 72.7. Inside this category, SWE-bench Verified 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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