Model comparison
Qwen3.6-27B vs Step 3.7 Flash
Head-to-head evidence from 24 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: Qwen3.6-27B #27; Step 3.7 Flash unranked
BenchAlign evidence: Qwen3.6-27B estimated; Step 3.7 Flash estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Qwen3.6-27B and Step 3.7 Flash share 24 comparable benchmark results. 2 of 8 categories are comparable. 31 results are unique to Qwen3.6-27B; 6 to Step 3.7 Flash.
Updated July 16, 2026- Shared results
- 24
- Qwen3.6-27B only
- 31
- Step 3.7 Flash only
- 6
- Comparable categories
- 2 / 8
Pick Qwen3.6-27B if you want the stronger benchmark profile. Step 3.7 Flash only becomes the better choice if agentic is the priority.
Confidence note. This is a partial-evidence comparison with 24 shared benchmark results across 6 evidence categories; 2 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 provisional aggregate, 66 to 57. 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 56.3. The single biggest benchmark swing on the page is SWE-bench Pro, 53.5% to 56.3%. Step 3.7 Flash does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Step 3.7 Flash is also the more expensive model on tokens at $0.20 input / $1.15 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 gives you the larger context window at 262K, compared with 256K for Step 3.7 Flash.
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 | Qwen3.6-27B | Δ | Step 3.7 Flash |
|---|---|---|---|
| Coding | Qwen3.6-27B77.5 | Margin← 21.2 | Step 3.7 Flash56.3 |
| Agentic | Qwen3.6-27B59.3 | Margin→ 7.1 | Step 3.7 Flash66.4 |
| Knowledge | Qwen3.6-27B53.6 | MarginNo overlap | Step 3.7 FlashNot measured |
| Math | Qwen3.6-27B89.2 | MarginNo overlap | Step 3.7 FlashNot measured |
| Multimodal | Qwen3.6-27B76.7 | MarginNo overlap | Step 3.7 FlashNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Pro
CodingA 53.5%B 56.3%Winner: Step 3.7 FlashΔ 2.8SWE-bench Pro: Qwen3.6-27B scored 53.5%; Step 3.7 Flash scored 56.3%. Step 3.7 Flash wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 59.3%B 59.5%Winner: Step 3.7 FlashΔ 0.2Terminal-Bench 2.0: Qwen3.6-27B scored 59.3%; Step 3.7 Flash scored 59.5%. Step 3.7 Flash wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Qwen3.6-27B | Step 3.7 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Qwen3.6-27B$0 input / $0 output | Step 3.7 Flash$0.2 input / $1.15 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | Qwen3.6-27BNot available | Step 3.7 FlashNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Qwen3.6-27BNot available | Step 3.7 FlashNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Qwen3.6-27B262K | Step 3.7 Flash256K | Qwen3.6-27B lists the larger context window. |
Benchmark Deep Dive
AgenticStep 3.7 Flash wins15 benchmarks
| Benchmark | Qwen3.6-27B | Step 3.7 Flash | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 59.3% | 59.5% | Step 3.7 Flash leads |
| Claw-EvalSource | 72.4% | 67.1% | Qwen3.6-27B leads |
| QwenClawBenchSource | 53.4% | — | Not comparable |
| QwenWebBenchSource | 1487 | — | Not comparable |
| AndroidWorldSource | 70.3% | — | Not comparable |
| AA Agentic IndexSource | 27.0% | 21.5% | Qwen3.6-27B leads |
| τ²-bench resultsSource | 94.2% | 98.5% | Step 3.7 Flash leads |
| GDPval-AASource | 32.0% | 25.9% | Qwen3.6-27B leads |
| GDPval-AASource | 1140 | 1017 | Qwen3.6-27B leads |
| Gert LabsSource | 54.84% | 51.57% | Qwen3.6-27B leads |
| BrowseCompSource | — | 75.8% | Not comparable |
| DeepSearchQASource | — | 92.8% | Not comparable |
| ToolathlonSource | — | 49.5% | Not comparable |
| HLE w/ toolsSource | — | 47.2% | Not comparable |
| APEX-Agents-AASource | — | 14.8% | Not comparable |
CodingQwen3.6-27B wins9 benchmarks
| Benchmark | Qwen3.6-27B | Step 3.7 Flash | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.2% | — | Not comparable |
| SWE MultilingualSource | 71.3% | — | Not comparable |
| SWE-bench ProSource | 53.5% | 56.3% | Step 3.7 Flash leads |
| Terminal-Bench 2.0Source | 59.3% | 59.5% | Step 3.7 Flash leads |
| LiveCodeBenchSource | 83.9% | — | Not comparable |
| NL2RepoSource | 36.2% | — | Not comparable |
| AA Coding IndexSource | 53.7% | 39.6% | Qwen3.6-27B leads |
| Terminal-Bench HardSource | 34.8% | 35.6% | Step 3.7 Flash leads |
| AA-SciCodeSource | 39.8% | 40.0% | Step 3.7 Flash leads |
Reasoning2 benchmarks
Knowledge12 benchmarks
| Benchmark | Qwen3.6-27B | Step 3.7 Flash | Result |
|---|---|---|---|
| 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 |
| Artificial Analysis Intelligence IndexSource | 37.0% | 30.3% | Qwen3.6-27B leads |
| AA-GPQA DiamondSource | 84.2% | 80.9% | Qwen3.6-27B leads |
| AA-HLESource | 21.6% | 19.9% | Qwen3.6-27B leads |
| AA-Omniscience IndexSource | -19.8% | -37.5% | Qwen3.6-27B leads |
| AA-Omniscience AccuracySource | 19.2% | 25.4% | Step 3.7 Flash leads |
| AA-Omniscience Hallucination RateSource | 48.3% | 84.4% | Qwen3.6-27B leads |
Math5 benchmarks
Multimodal17 benchmarks
| Benchmark | Qwen3.6-27B | Step 3.7 Flash | Result |
|---|---|---|---|
| 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% | 79.2% | Step 3.7 Flash leads |
| 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% | 95.3% | Step 3.7 Flash leads |
| AA-MMMU-ProSource | 74.6% | 75.3% | Step 3.7 Flash leads |
| Design Arena WebsiteSource | — | 1218 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Qwen3.6-27B | Step 3.7 Flash | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.6% | 67.3% | Qwen3.6-27B leads |
Frequently Asked Questions (3)
Which is better, Qwen3.6-27B or Step 3.7 Flash?
Qwen3.6-27B is ahead on BenchLM's provisional leaderboard, 66 to 57. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 53.5% and 56.3%.
Which is better for coding, Qwen3.6-27B or Step 3.7 Flash?
Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 versus 56.3. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Qwen3.6-27B or Step 3.7 Flash?
Step 3.7 Flash has the edge for agentic tasks in this comparison, averaging 66.4 versus 59.3. Inside this category, GDPval-AA 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.
Related Comparisons
Explore More
The AI models change fast. We track them for you.
A weekly brief for engineers and researchers covering new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.