Model comparison
GLM-5 vs Qwen3 235B 2507 (Reasoning)
Head-to-head evidence from 2 shared benchmark results across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (Supported); Qwen3 235B 2507 (Reasoning) #66 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Qwen3 235B 2507 (Reasoning) share 2 comparable benchmark results. 1 of 8 categories are comparable. 47 results are unique to GLM-5; 0 to Qwen3 235B 2507 (Reasoning).
Updated July 20, 2026- Shared results
- 2
- GLM-5 only
- 47
- Qwen3 235B 2507 (Reasoning) only
- 0
- Comparable categories
- 1 / 8
Pick GLM-5 if you want the stronger benchmark profile. Qwen3 235B 2507 (Reasoning) only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 2 shared benchmark results across 1 evidence category; 1 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 is clearly ahead on the BenchAlign aggregate, 66.06 to 58.01. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in mathematics, where it averages 56.3 against 6.4. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 16.434% to 8.481%.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3 235B 2507 (Reasoning). That is roughly Infinityx on output cost alone. Qwen3 235B 2507 (Reasoning) is the reasoning model in the pair, while GLM-5 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. GLM-5 gives you the larger context window at 200K, compared with 128K for Qwen3 235B 2507 (Reasoning).
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 | Δ | Qwen3 235B 2507 (Reasoning) |
|---|---|---|---|
| Math | GLM-556.3 | Margin← 49.9 | Qwen3 235B 2507 (Reasoning)6.4 |
| Agentic | GLM-556.2 | MarginNo overlap | Qwen3 235B 2507 (Reasoning)Not measured |
| Coding | GLM-566.3 | MarginNo overlap | Qwen3 235B 2507 (Reasoning)Not measured |
| Reasoning | GLM-560.8 | MarginNo overlap | Qwen3 235B 2507 (Reasoning)Not measured |
| Knowledge | GLM-566.4 | MarginNo overlap | Qwen3 235B 2507 (Reasoning)Not measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Qwen3 235B 2507 (Reasoning)Not measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | Qwen3 235B 2507 (Reasoning)Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 16.434%B 8.481%Winner: GLM-5Δ 8FrontierMath v2 (Tiers 1-3): GLM-5 scored 16.434%; Qwen3 235B 2507 (Reasoning) scored 8.481%. GLM-5 wins this benchmark. - Source ↗
FrontierMath v2 (Tier 4)
MathA 2.100%B 0.000%Winner: GLM-5Δ 2.1FrontierMath v2 (Tier 4): GLM-5 scored 2.100%; Qwen3 235B 2507 (Reasoning) scored 0.000%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Qwen3 235B 2507 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Qwen3 235B 2507 (Reasoning)$0 input / $0 output | Qwen3 235B 2507 (Reasoning) has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | Qwen3 235B 2507 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Qwen3 235B 2507 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Qwen3 235B 2507 (Reasoning)128K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | GLM-5 | Qwen3 235B 2507 (Reasoning) | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | — | Not comparable |
| Claw-EvalSource | 57.7% | — | Not comparable |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| τ³-bench resultsSource | 65.6% | — | Not comparable |
| DeepPlanningSource | 14.6% | — | Not comparable |
| ToolathlonSource | 38% | — | Not comparable |
| MCP AtlasSource | 31.1% | — | Not comparable |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | — | Not comparable |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
Coding7 benchmarks
| Benchmark | GLM-5 | Qwen3 235B 2507 (Reasoning) | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | — | Not comparable |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | — | Not comparable |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| AA-SciCodeSource | 46.2% | — | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | Qwen3 235B 2507 (Reasoning) | Result |
|---|---|---|---|
| GPQASource | 86% | — | Not comparable |
| GPQA-DSource | 86.0% | — | Not comparable |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 39.5% | — | Not comparable |
| AA-GPQA DiamondSource | 82.0% | — | Not comparable |
| AA-HLESource | 27.2% | — | Not comparable |
| AA-Omniscience IndexSource | 2.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 26.9% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 34.0% | — | Not comparable |
MathGLM-5 wins8 benchmarks
| Benchmark | GLM-5 | Qwen3 235B 2507 (Reasoning) | Result |
|---|---|---|---|
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 93.3% | — | Not comparable |
| HMMT Feb 2025Source | 97.5% | — | Not comparable |
| HMMT Nov 2025Source | 96.9% | — | Not comparable |
| HMMT Feb 2026Source | 86.4% | — | Not comparable |
| MMAnswerBenchSource | 82.5% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 16.434% | 8.481% | GLM-5 leads |
| FrontierMath v2 (Tier 4)Source | 2.100% | 0.000% | GLM-5 leads |
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5 | Qwen3 235B 2507 (Reasoning) | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1280 | — | Not comparable |
Frequently Asked Questions (2)
Which is better, GLM-5 or Qwen3 235B 2507 (Reasoning)?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 66.06 to 58.01. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 16.434% and 8.481%.
Which is better for math, GLM-5 or Qwen3 235B 2507 (Reasoning)?
GLM-5 has the edge for math in this comparison, averaging 56.3 versus 6.4. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.
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