Model comparison
Claude Opus 4.7 (Adaptive) vs GLM-5
Head-to-head evidence from 20 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); GLM-5 #28 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.7 (Adaptive) and GLM-5 share 20 comparable benchmark results. 4 of 8 categories are comparable. 18 results are unique to Claude Opus 4.7 (Adaptive); 29 to GLM-5.
Updated July 23, 2026- Shared results
- 20
- Claude Opus 4.7 (Adaptive) only
- 18
- GLM-5 only
- 29
- Comparable categories
- 4 / 8
Pick Claude Opus 4.7 (Adaptive) if you want the stronger benchmark profile. GLM-5 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 20 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
Claude Opus 4.7 (Adaptive) has the cleaner BenchAlign overall profile here, landing at 66.27 versus 66.06. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Claude Opus 4.7 (Adaptive)'s sharpest advantage is in agentic, where it averages 75.1 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 69.4% to 56.2%. GLM-5 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. That is roughly 7.8x on output cost alone. Claude Opus 4.7 (Adaptive) 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. Claude Opus 4.7 (Adaptive) gives you the larger context window at 1M, compared with 200K for GLM-5.
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 Opus 4.7 (Adaptive) | Δ | GLM-5 |
|---|---|---|---|
| Agentic | Claude Opus 4.7 (Adaptive)75.1 | Margin← 18.9 | GLM-556.2 |
| Reasoning | Claude Opus 4.7 (Adaptive)75.8 | Margin← 15.0 | GLM-560.8 |
| Coding | Claude Opus 4.7 (Adaptive)78.6 | Margin← 12.3 | GLM-566.3 |
| Knowledge | Claude Opus 4.7 (Adaptive)60.0 | Margin→ 6.4 | GLM-566.4 |
| Math | Claude Opus 4.7 (Adaptive)Not measured | MarginNo overlap | GLM-556.3 |
| Multilingual | Claude Opus 4.7 (Adaptive)Not measured | MarginNo overlap | GLM-583.1 |
| Multimodal | Claude Opus 4.7 (Adaptive)65.1 | MarginNo overlap | GLM-5Not measured |
| Inst. Following | Claude Opus 4.7 (Adaptive)Not measured | MarginNo overlap | GLM-592.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 69.4%B 56.2%Winner: Claude Opus 4.7 (Adaptive)Δ 13.2Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; GLM-5 scored 56.2%. Claude Opus 4.7 (Adaptive) wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 87.6%B 77.8%Winner: Claude Opus 4.7 (Adaptive)Δ 9.8SWE-bench Verified: Claude Opus 4.7 (Adaptive) scored 87.6%; GLM-5 scored 77.8%. Claude Opus 4.7 (Adaptive) wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 64.3%B 55.1%Winner: Claude Opus 4.7 (Adaptive)Δ 9.2SWE-bench Pro: Claude Opus 4.7 (Adaptive) scored 64.3%; GLM-5 scored 55.1%. Claude Opus 4.7 (Adaptive) wins this benchmark. - Source ↗
GPQA
KnowledgeA 94.2%B 86%Winner: Claude Opus 4.7 (Adaptive)Δ 8.2GPQA: Claude Opus 4.7 (Adaptive) scored 94.2%; GLM-5 scored 86%. Claude Opus 4.7 (Adaptive) wins this benchmark. - Source ↗
HLE
KnowledgeA 54.7%B 50.4%Winner: Claude Opus 4.7 (Adaptive)Δ 4.3HLE: Claude Opus 4.7 (Adaptive) scored 54.7%; GLM-5 scored 50.4%. Claude Opus 4.7 (Adaptive) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Opus 4.7 (Adaptive) | GLM-5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.7 (Adaptive)$5 input / $25 output | GLM-5$1 input / $3.2 output | GLM-5 has the lower combined listed price. |
| Generation speedtokens per second | Claude Opus 4.7 (Adaptive)Not available | GLM-574 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.7 (Adaptive)Not available | GLM-51.64 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.7 (Adaptive)1M | GLM-5200K | Claude Opus 4.7 (Adaptive) lists the larger context window. |
Benchmark Deep Dive
AgenticClaude Opus 4.7 (Adaptive) wins21 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GLM-5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 69.4% | 56.2% | Claude Opus 4.7 (Adaptive) leads |
| BrowseCompSource | 79.3% | — | Not comparable |
| MCP AtlasSource | 77.3% | 31.1% | Claude Opus 4.7 (Adaptive) leads |
| OSWorld-VerifiedSource | 78% | — | Not comparable |
| CyberGymSource | 73.1% | 43.2% | Claude Opus 4.7 (Adaptive) leads |
| AA Agentic IndexSource | 44.4% | — | Not comparable |
| τ²-bench resultsSource | 88.6% | 98.2% | GLM-5 leads |
| GDPval-AASource | 49.8% | — | Not comparable |
| GDPval-AASource | 1495 | — | Not comparable |
| OSWorld 2.0Source | 18.2% | — | Not comparable |
| JobBenchSource | 45.9% | — | Not comparable |
| AA ITBenchSource | 46.7% | — | 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-TasksSource | — | 60.8% | Not comparable |
| WideResearchSource | — | 69.8% | Not comparable |
| APEX-Agents-AASource | — | 14.5% | Not comparable |
| Gert LabsSource | — | 50.99% | Not comparable |
CodingClaude Opus 4.7 (Adaptive) wins9 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GLM-5 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 87.6% | 77.8% | Claude Opus 4.7 (Adaptive) leads |
| SWE-bench ProSource | 64.3% | 55.1% | Claude Opus 4.7 (Adaptive) leads |
| Terminal-Bench 2.0Source | 69.4% | — | Not comparable |
| AA Coding IndexSource | 73.6% | — | Not comparable |
| AA-SciCodeSource | 54.5% | 46.2% | Claude Opus 4.7 (Adaptive) leads |
| SWE-bench Verified*Source | — | 72.8% | Not comparable |
| SWE MultilingualSource | — | 73.3% | Not comparable |
| SWE-RebenchSource | — | 62.8% | Not comparable |
| React Native EvalsSource | — | 74.8% | Not comparable |
ReasoningClaude Opus 4.7 (Adaptive) wins6 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GLM-5 | Result |
|---|---|---|---|
| MRCR v2 128K-256KSource | 59.2% | — | Not comparable |
| ARC-AGI-2Source | 75.8% | — | Not comparable |
| AA-LCRSource | 70.3% | 63.3% | Claude Opus 4.7 (Adaptive) leads |
| CritPtSource | 12.0% | 2.0% | Claude Opus 4.7 (Adaptive) leads |
| LongBench v2Source | — | 60.8% | Not comparable |
| AI-NeedleSource | — | 63.3% | Not comparable |
KnowledgeGLM-5 wins13 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GLM-5 | Result |
|---|---|---|---|
| GPQASource | 94.2% | 86% | Claude Opus 4.7 (Adaptive) leads |
| GPQA-DSource | 94.2% | 86.0% | Claude Opus 4.7 (Adaptive) leads |
| HLESource | 54.7% | 50.4% | Claude Opus 4.7 (Adaptive) leads |
| HLE w/o toolsSource | 46.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 53.5% | 39.5% | Claude Opus 4.7 (Adaptive) leads |
| AA-GPQA DiamondSource | 91.4% | 82.0% | Claude Opus 4.7 (Adaptive) leads |
| AA-HLESource | 39.6% | 27.2% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience IndexSource | 26.2% | 2.0% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience AccuracySource | 45.8% | 26.9% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience Hallucination RateSource | 36.2% | 34.0% | GLM-5 leads |
| SuperGPQASource | — | 66.8% | Not comparable |
| MMLU-ProSource | — | 85.7% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 85.8% | Not comparable |
Math9 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GLM-5 | Result |
|---|---|---|---|
| FrontierMath (legacy)Source | 43.8% | — | Not comparable |
| 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% | Not comparable |
| FrontierMath v2 (Tier 4)Source | — | 2.100% | Not comparable |
Multilingual2 benchmarks
Multimodal5 benchmarks
Frequently Asked Questions (5)
Which is better, Claude Opus 4.7 (Adaptive) or GLM-5?
Claude Opus 4.7 (Adaptive) is ahead on BenchLM's BenchAlign leaderboard, 66.27 to 66.06. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 69.4% and 56.2%.
Which is better for knowledge tasks, Claude Opus 4.7 (Adaptive) or GLM-5?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 60. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, Claude Opus 4.7 (Adaptive) or GLM-5?
Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 66.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for reasoning, Claude Opus 4.7 (Adaptive) or GLM-5?
Claude Opus 4.7 (Adaptive) has the edge for reasoning in this comparison, averaging 75.8 versus 60.8. Inside this category, CritPt is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Claude Opus 4.7 (Adaptive) or GLM-5?
Claude Opus 4.7 (Adaptive) has the edge for agentic tasks in this comparison, averaging 75.1 versus 56.2. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
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