Model comparison
GLM-4.7 vs Qwen3 235B 2507
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-4.7 #42 (Supported); Qwen3 235B 2507 #78 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and Qwen3 235B 2507 share 2 comparable benchmark results. 1 of 8 categories are comparable. 28 results are unique to GLM-4.7; 2 to Qwen3 235B 2507.
Updated July 22, 2026- Shared results
- 2
- GLM-4.7 only
- 28
- Qwen3 235B 2507 only
- 2
- Comparable categories
- 1 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
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-4.7 is clearly ahead on the BenchAlign aggregate, 61.16 to 56.02. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7 is the reasoning model in the pair, while Qwen3 235B 2507 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-4.7 gives you the larger context window at 200K, compared with 128K for Qwen3 235B 2507.
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-4.7 | Δ | Qwen3 235B 2507 |
|---|---|---|---|
| Knowledge | GLM-4.751.8 | Margin→ 27.1 | Qwen3 235B 250778.9 |
| Agentic | GLM-4.745.7 | MarginNo overlap | Qwen3 235B 2507Not measured |
| Coding | GLM-4.775.4 | MarginNo overlap | Qwen3 235B 2507Not measured |
| Math | GLM-4.71.8 | MarginNo overlap | Qwen3 235B 2507Not measured |
| Multilingual | GLM-4.7Not measured | MarginNo overlap | Qwen3 235B 250779.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | Qwen3 235B 2507 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Qwen3 235B 2507$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | GLM-4.782 tok/s | Qwen3 235B 2507Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Qwen3 235B 2507Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | Qwen3 235B 2507128K | GLM-4.7 lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GLM-4.7 | Qwen3 235B 2507 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | — | Not comparable |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | — | Not comparable |
| τ²-bench resultsSource | 95.9% | — | Not comparable |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 1165 | — | Not comparable |
Coding6 benchmarks
Reasoning2 benchmarks
KnowledgeQwen3 235B 2507 wins10 benchmarks
| Benchmark | GLM-4.7 | Qwen3 235B 2507 | Result |
|---|---|---|---|
| GPQASource | 85.7% | 77.5% | GLM-4.7 leads |
| MMLU-ProSource | 84.3% | 83% | GLM-4.7 leads |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | — | Not comparable |
| AA-GPQA DiamondSource | 85.9% | — | Not comparable |
| AA-HLESource | 25.1% | — | Not comparable |
| AA-Omniscience IndexSource | -34.6% | — | Not comparable |
| AA-Omniscience AccuracySource | 29.3% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 90.3% | — | Not comparable |
| SuperGPQASource | — | 62.6% | Not comparable |
Math3 benchmarks
Multilingual1 benchmarks
| Benchmark | GLM-4.7 | Qwen3 235B 2507 | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 79.4% | Not comparable |
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | Qwen3 235B 2507 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1255 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | Qwen3 235B 2507 | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | — | Not comparable |
Frequently Asked Questions (2)
Which is better, GLM-4.7 or Qwen3 235B 2507?
GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 61.16 to 56.02. The biggest single separator in this matchup is GPQA, where the scores are 85.7% and 77.5%.
Which is better for knowledge tasks, GLM-4.7 or Qwen3 235B 2507?
Qwen3 235B 2507 has the edge for knowledge tasks in this comparison, averaging 78.9 versus 51.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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