Model comparison
GLM-5.1 vs Qwen3.6-27B
Head-to-head evidence from 25 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5.1 #18 (Supported); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.1 and Qwen3.6-27B share 25 comparable benchmark results. 4 of 8 categories are comparable. 11 results are unique to GLM-5.1; 29 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 25
- GLM-5.1 only
- 11
- Qwen3.6-27B only
- 29
- Comparable categories
- 4 / 8
Pick GLM-5.1 if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 25 shared benchmark results across 6 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GLM-5.1 is clearly ahead on the BenchAlign aggregate, 67.74 to 53.82. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in agentic, where it averages 65.4 against 59.3. The single biggest benchmark swing on the page is HLE, 52.3% to 24%. Qwen3.6-27B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 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 203K for GLM-5.1.
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 | GLM-5.1 | Δ | Qwen3.6-27B |
|---|---|---|---|
| Math | GLM-5.162.0 | Margin→ 27.2 | Qwen3.6-27B89.2 |
| Coding | GLM-5.161.3 | Margin→ 16.2 | Qwen3.6-27B77.5 |
| Agentic | GLM-5.165.4 | Margin← 6.1 | Qwen3.6-27B59.3 |
| Knowledge | GLM-5.152.3 | Margin→ 1.0 | Qwen3.6-27B53.3 |
| Multimodal | GLM-5.1Not measured | MarginNo overlap | Qwen3.6-27B76.7 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 52.3%B 24%Winner: GLM-5.1Δ 28.3HLE: GLM-5.1 scored 52.3%; Qwen3.6-27B scored 24%. GLM-5.1 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 58.4%B 53.5%Winner: GLM-5.1Δ 4.9SWE-bench Pro: GLM-5.1 scored 58.4%; Qwen3.6-27B scored 53.5%. GLM-5.1 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 63.5%B 59.3%Winner: GLM-5.1Δ 4.2Terminal-Bench 2.0: GLM-5.1 scored 63.5%; Qwen3.6-27B scored 59.3%. GLM-5.1 wins this benchmark. - Source ↗
HMMT Feb 2026
MathA 82.6%B 84.3%Winner: Qwen3.6-27BΔ 1.7HMMT Feb 2026: GLM-5.1 scored 82.6%; Qwen3.6-27B scored 84.3%. Qwen3.6-27B wins this benchmark. - Source ↗
AIME26
MathA 95.3%B 94.1%Winner: GLM-5.1Δ 1.2AIME26: GLM-5.1 scored 95.3%; Qwen3.6-27B scored 94.1%. GLM-5.1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5.1 | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.1$1.4 input / $4.4 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | GLM-5.1Not available | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.1Not available | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.1203K | Qwen3.6-27B262K | Qwen3.6-27B lists the larger context window. |
Benchmark Deep Dive
AgenticGLM-5.1 wins15 benchmarks
| Benchmark | GLM-5.1 | Qwen3.6-27B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 63.5% | 59.3% | GLM-5.1 leads |
| BrowseCompSource | 68% | — | Not comparable |
| τ³-bench resultsSource | 70.6% | — | Not comparable |
| MCP AtlasSource | 71.8% | — | Not comparable |
| CyberGymSource | 68.7% | — | Not comparable |
| Claw-EvalSource | 62.3% | 72.4% | Qwen3.6-27B leads |
| AA Agentic IndexSource | 29.9% | 27.0% | GLM-5.1 leads |
| τ²-bench resultsSource | 97.7% | 94.2% | GLM-5.1 leads |
| GDPval-AASource | 37.8% | 32.0% | GLM-5.1 leads |
| Gert LabsSource | 60.11% | 54.84% | GLM-5.1 leads |
| GDPval-AASource | 1257 | 1140 | GLM-5.1 leads |
| ResearchClawBenchSource | 18.2% | — | Not comparable |
| QwenClawBenchSource | — | 53.4% | Not comparable |
| QwenWebBenchSource | — | 1487 | Not comparable |
| AndroidWorldSource | — | 70.3% | Not comparable |
CodingQwen3.6-27B wins10 benchmarks
| Benchmark | GLM-5.1 | Qwen3.6-27B | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.4% | 53.5% | GLM-5.1 leads |
| NL2RepoSource | 42.7% | 36.2% | GLM-5.1 leads |
| SWE-RebenchSource | 62.7% | — | Not comparable |
| Vibe Code BenchSource | 31.46% | — | Not comparable |
| AA Coding IndexSource | 55.8% | 53.7% | GLM-5.1 leads |
| AA-SciCodeSource | 43.8% | 39.8% | GLM-5.1 leads |
| SWE-bench VerifiedSource | — | 77.2% | Not comparable |
| SWE MultilingualSource | — | 71.3% | Not comparable |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| LiveCodeBenchSource | — | 83.9% | Not comparable |
Reasoning2 benchmarks
KnowledgeQwen3.6-27B wins13 benchmarks
| Benchmark | GLM-5.1 | Qwen3.6-27B | Result |
|---|---|---|---|
| GPQA-DSource | 86.2% | — | Not comparable |
| HLESource | 52.3% | 24% | GLM-5.1 leads |
| Artificial Analysis Intelligence IndexSource | 40.2% | 37.0% | GLM-5.1 leads |
| AA-GPQA DiamondSource | 86.8% | 84.2% | GLM-5.1 leads |
| AA-HLESource | 28.0% | 21.6% | GLM-5.1 leads |
| AA-Omniscience IndexSource | 1.9% | -19.8% | GLM-5.1 leads |
| AA-Omniscience AccuracySource | 24.2% | 19.2% | GLM-5.1 leads |
| AA-Omniscience Hallucination RateSource | 29.4% | 48.3% | GLM-5.1 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 |
MathQwen3.6-27B wins7 benchmarks
| Benchmark | GLM-5.1 | Qwen3.6-27B | Result |
|---|---|---|---|
| AIME26Source | 95.3% | 94.1% | GLM-5.1 leads |
| HMMT Nov 2025Source | 94.0% | 90.7% | GLM-5.1 leads |
| HMMT Feb 2026Source | 82.6% | 84.3% | Qwen3.6-27B leads |
| MMAnswerBenchSource | 83.8% | 80.8% | GLM-5.1 leads |
| FrontierMath v2 (Tiers 1-3)Source | 33.448% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 12.500% | — | Not comparable |
| HMMT Feb 2025Source | — | 93.8% | Not comparable |
Multimodal17 benchmarks
| Benchmark | GLM-5.1 | Qwen3.6-27B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1305 | — | 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 |
| AA-MMMU-ProSource | — | 74.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-5.1 | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.3% | 67.6% | GLM-5.1 leads |
Frequently Asked Questions (5)
Which is better, GLM-5.1 or Qwen3.6-27B?
GLM-5.1 is ahead on BenchLM's BenchAlign leaderboard, 67.74 to 53.82. The biggest single separator in this matchup is HLE, where the scores are 52.3% and 24%.
Which is better for knowledge tasks, GLM-5.1 or Qwen3.6-27B?
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 53.3 versus 52.3. Inside this category, HLE is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5.1 or Qwen3.6-27B?
Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 versus 61.3. Inside this category, NL2Repo is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5.1 or Qwen3.6-27B?
Qwen3.6-27B has the edge for math in this comparison, averaging 89.2 versus 62. Inside this category, HMMT Nov 2025 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5.1 or Qwen3.6-27B?
GLM-5.1 has the edge for agentic tasks in this comparison, averaging 65.4 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.
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