Claude Opus 4.6 vs GLM-5
Full profiles: Claude Opus 4.6 (Anthropic) and GLM-5 (Zhipu AI): every benchmark, price and rank.
Updated · Data from 11 sources
As of 29 Oct 2026, Claude Opus 4.6 and GLM-5 are neck and neck on quality (62.6 vs 60.2 on the BenchLeader Index, inside the ±4.3 margin), and GLM-5 is 6.5× cheaper at $1.55 vs $10.00 per 1M tokens blended. Choose Claude Opus 4.6 for coding (63 vs 59) and fast first answers (1.2 s vs 46.9 s); choose GLM-5 for reasoning (68 vs 66), where it also streams ~1.5× faster.
Key facts (as of 29 Oct 2026)
- Claude Opus 4.6 scores 62.6 on the BenchLeader Index and GLM-5 scores 60.2 (BenchLeader, 29 Oct 2026).
- Claude Opus 4.6 costs $10.00 per 1M tokens blended (list price, 29 Oct 2026).
- GLM-5 costs $1.55 per 1M tokens blended (list price, 29 Oct 2026).
- GLM-5 streams ~1.5× faster than Claude Opus 4.6 (Artificial Analysis, 7-day median, 29 Oct 2026).
Claude Opus 4.6 benchmarks high Anthropic
GLM-5 benchmarks thinking Zhipu AIOpen in the comparison toolWhich to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | Claude Opus 4.6 | 63 vs 59 category score | Medium |
| Maths | Either | 63 vs 63 category score | Either |
| Reasoning | GLM-5 | 68 vs 66 category score | Medium |
| Chat quality (human preference) | – | Only Claude Opus 4.6 has an Arena rating (70 category score) | Not comparable |
| Lowest cost | GLM-5 | $1.55 vs $10.00 per 1M blended; agentic coding session $0.073 vs $0.400 | – |
| Fastest first answer | Claude Opus 4.6 | 1.2 s (Claude Opus 4.6 (high)) vs 46.9 s (GLM-5 (thinking)) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- Claude Opus 4.6 (high) and GLM-5 (thinking) are neck and neck on quality (62.6 vs 60.2, within the ±4.3 margin).
- Claude Opus 4.6 (high) is stronger in coding, human preference, maths, multimodal.
- GLM-5 (thinking) is stronger in reasoning, agents & tools, instruction following, knowledge, long context.
- GLM-5 (thinking) is 6.5× cheaper ($1.55 vs $10.00 per 1M blended).
- GLM-5 (thinking) streams 2.0× faster (69 vs 35 tokens per second).
At a glance
| Spec | Claude Opus 4.6 | GLM-5 |
|---|---|---|
| Developer | Anthropic | Zhipu AI |
| Released | 5 Feb 2026 | 11 Feb 2026 |
| Context window (tokens) | 1,000,000 | 204,800 |
| Compared at | Claude Opus 4.6 (high) | GLM-5 (thinking) |
| Open weights | No | Yes |
| Price in / out / cached input per 1M | $5.00 / $25.00 / $0.500 | $1.00 / $3.20 / $0.200 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | Claude Opus 4.6 (high) | GLM-5 (thinking) | Leader |
|---|---|---|---|
| Reasoning | 65.5 | 68.2 | GLM-5 |
| Coding | 62.5 | 59.1 | Claude Opus 4.6 |
| Agents & tools | – | 58.0 | not comparable |
| Maths | 63.4 | 63.2 | level |
| Knowledge | – | 57.3 | not comparable |
| Instruction following | – | 70.8 | not comparable |
| Human preference | 69.8 | – | not comparable |
| Multimodal | 66.6 | – | not comparable |
| Long context | – | 62.6 | not comparable |
Where sources disagree
Across the 0 benchmarks both have, Claude Opus 4.6 leads 0, GLM-5 leads 0. Every overall index that rates both puts Claude Opus 4.6 ahead.
| Overall index | Claude Opus 4.6 (high) | GLM-5 (thinking) | Leader |
|---|---|---|---|
| BenchLeader Index | 62.6 | 60.2 | neck and neck |
| Arena Text | 1503 | 1457† | Claude Opus 4.6 |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.
| Measure | Claude Opus 4.6 (high) | GLM-5 (thinking) |
|---|---|---|
| Blended price per 1M tokens | $10.00 | $1.55 |
| Input / output per 1M | $5.00 / $25.00 | $1.00 / $3.20 |
| Cached input per 1M | $0.500 | $0.200 |
| Price per task: chat reply | $0.0095 | $0.0014 |
| Price per task: summarize a 30-page report | $0.075 | $0.014 |
| Price per task: code edit | $0.068 | $0.011 |
| Price per task: agentic coding session | $0.400 | $0.073 |
| Price per task: structured extraction | $0.015 | $0.0026 |
| Output speed | 35 tok/s | 69 tok/s |
| Time to first answer | 1.2 s | 46.9 s |
| Time to a complete answer | – | 54.2 s |
Each model is compared at its best-ranked reasoning-effort setting. Claude Opus 4.6 (high) scores 62.6 and takes 1 s to its first answer token; Claude Opus 4.6 (thinking) scores 62.4 in 1 s, Claude Opus 4.6 scores 62.4 in 1 s. GLM-5 (thinking) scores 60.2 and takes 47 s to its first answer token; GLM-5 scores 54.9 in 2 s. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
108 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | Claude Opus 4.6 (high) | GLM-5 (thinking) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 62.6 | 60.2 |
| Coding score | BenchLeader | 62.5 | 59.1 |
| Human preference score | BenchLeader | 69.8 | – |
| Maths score | BenchLeader | 63.4 | 63.2 |
| Multimodal score | BenchLeader | 66.6 | – |
| Reasoning score | BenchLeader | 65.5 | 68.2 |
| Agents & tools score | BenchLeader | – | 58.0 |
| Instruction following score | BenchLeader | – | 70.8 |
| Knowledge score | BenchLeader | – | 57.3 |
| Long context score | BenchLeader | – | 62.6 |
| Blended price $/M | BenchLeader | $10.00 | $1.55 |
| Output speed | BenchLeader | 35 tok/s | 69 tok/s |
| Time to first answer | BenchLeader | 1.2 s | 46.9 s |
| Context window | BenchLeader | 1,000,000 tokens | 204,800 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 90.5%† | 87.8%† |
| Humanity's Last Exam | Scale AI / CAIS | 19.0%† | – |
| SimpleBench | SimpleBench | 67.6%† | 53.2%† |
| Arena Hard Prompts | Arena (LMArena) | 1531 | 1478† |
| LiveBench 1.0 Reasoning | LiveBench | 88.7% | – |
| GPQA Diamond (AA) | Artificial Analysis | 84.0%† | 82.0% |
| Humanity's Last Exam (AA) | Artificial Analysis | 19.1%† | 29.3% |
| GPQA Diamond (Vals) | Vals AI | 89.7%† | 83.3% |
| Kagi LLM Benchmark | Kagi LLM Benchmark | 72.4%† | 75.0% |
| ARC-AGI-1 | ARC Prize | 94.0% | 44.7%† |
| ARC-AGI-2 | ARC Prize | 69.2% | 4.9%† |
| ARC-AGI-3 | ARC Prize | 0.5%† | – |
| CritPt | Artificial Analysis | 2.8%† | 2.0% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 21.0%† | – |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 15.0%† | – |
| CL-bench | Epoch AI Benchmarking Hub | 20.7%† | 18.7%† |
| CL-bench Life | Epoch AI Benchmarking Hub | 17.0% | – |
| LMCA | Epoch AI Benchmarking Hub | 55.8%† | – |
| DTBench | Epoch AI Benchmarking Hub | 91.2%† | – |
| ForecastBench | ForecastBench | 63.4%† | 63.7%† |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | 78.7%† | 72.1%† |
| WeirdML | WeirdML | 78.0% | 48.2%† |
| FrontierCode | Cognition | 26.6%† | – |
| GSO-Bench | GSO-Bench | 41.2% | – |
| Arena Coding | Arena (LMArena) | 1548 | 1498† |
Show all 108 benchmarks
| Metric | Publisher | Claude Opus 4.6 (high) | GLM-5 (thinking) |
|---|---|---|---|
| Coding | |||
| Arena WebDev | Arena (LMArena) | 1546 | 1436† |
| LiveBench 1.0 Coding | LiveBench | 78.2% | – |
| LiveCodeBench | Vals AI | 84.7%† | 81.9% |
| SWE-bench (Vals) | Vals AI | 78.2%† | 71.4% |
| SWE-Bench Pro | Scale AI SEAL | 51.9%† | – |
| SWE-bench Verified (bash only) | SWE-bench | 75.6%† | 72.8%† |
| SWE-bench Verified (any scaffold) | SWE-bench | 75.6%† | 72.8%† |
| Vibe Code Bench v1.1 | Vals AI | 57.6%† | 23.4% |
| SWE-Bench Pro (private) | Scale AI SEAL | 51.9%† | – |
| SWE Atlas: Codebase QnA | Scale AI SEAL | 33.3%† | 20.5%† |
| SWE Atlas: Refactoring | Scale AI SEAL | 35.6%† | 24.2%† |
| SWE Atlas: Test Writing | Scale AI SEAL | 36.7%† | 28.7%† |
| ALE-Bench | Epoch AI Benchmarking Hub | 996.5† | 765.6† |
| AlgoTune | AlgoTune | 1.5 | – |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 79.8%† | 52.4%† |
| Cybench | Cybench | 93.0%† | – |
| Remote Labor Index | Scale AI / CAIS | 4.2%† | – |
| APEX-Agents | Mercor | 46.3%† | – |
| LiveBench Agentic Coding | LiveBench | 49.0% | – |
| Terminal-Bench Hard | Artificial Analysis | 48.5%† | 43.2% |
| τ²-Bench Telecom (AA) | Artificial Analysis | 84.8%† | 98.3% |
| MCP Atlas | Scale AI SEAL | 76.8%† | – |
| HiL-Bench | Scale AI SEAL | 38.3%† | – |
| τ²-bench | τ²-bench | – | 81.0% |
| τ³-bench Banking | τ²-bench | 27.3%† | 9.8% |
| APEX-Agents (AA) | Artificial Analysis | 33.0%† | 14.4% |
| Terminal-Bench 2.0 (Vals) | Vals AI | 58.4%† | 49.4% |
| DeepResearch Bench | DeepResearch Bench | 55.3% | – |
| METR Time Horizons | METR | 78.9%† | – |
| GBAEval | Epoch AI Benchmarking Hub | 44.1%† | – |
| Vending-Bench 2 | Andon Labs | 8017.6† | 4432.1† |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 66.0%† | – |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 26.8%† | – |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 94.4%† | 80.0%† |
| ProofBench | Vals AI | 50.0%† | – |
| LiveBench Mathematics | LiveBench | 89.3% | – |
| AIME (Vals) | Vals AI | 95.6%† | 91.7% |
| AIME 2026 | MathArena | 96.7% | 96.7%† |
| HMMT February 2026 | MathArena | 96.2% | 86.4%† |
| USAMO 2026 | MathArena | 47.0% | 35.1%† |
| MathArena Apex | MathArena | 34.5% | 10.9%† |
| Arena Maths | Arena (LMArena) | 1517 | 1444† |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 47.0%† | – |
| LiveBench 1.0 Data Analysis | LiveBench | 69.9% | – |
| MMLU-Pro | Vals AI | 89.1%† | 86.0% |
| LegalBench | Vals AI | 85.3%† | 84.1% |
| CorpFin | Vals AI | 67.0%† | 62.9% |
| TaxEval | Vals AI | 76.0%† | 70.0% |
| MedQA | Vals AI | 95.4%† | 94.3% |
| PRBench Finance | Scale AI SEAL | 53.3%† | – |
| PRBench Legal | Scale AI SEAL | 52.3%† | – |
| MultiNRC | Scale AI SEAL | 48.3%† | – |
| CaseLaw v2 | Vals AI | 62.1%† | 52.5% |
| MedCode | Vals AI | 48.2%† | – |
| MedScribe | Vals AI | 86.7%† | – |
| DrugDiscoveryBench | Scale AI SEAL | 27.7%† | – |
| EBR-bench | Epoch AI Benchmarking Hub | 12.7%† | – |
| Global-MMLU-Lite (AA) | Artificial Analysis | 92.2%† | – |
| Instruction following | |||
| LiveBench Language | LiveBench | 83.3% | – |
| LiveBench Instruction Following | LiveBench | 63.3% | – |
| IFBench | Artificial Analysis | 44.6%† | 72.3% |
| MultiChallenge | Scale AI SEAL | 56.0%† | – |
| TutorBench | Scale AI SEAL | 53.5%† | – |
| Arena Instruction Following | Arena (LMArena) | 1511 | 1447† |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1503 | 1457† |
| EQ-Bench 4 | EQ-Bench | 1210† | – |
| Arena Creative Writing | Arena (LMArena) | 1499 | 1448† |
| Arena Multi-turn | Arena (LMArena) | 1517 | 1471† |
| Arena Longer Queries | Arena (LMArena) | 1521 | 1468† |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1316 | – |
| MMMU-Pro | Artificial Analysis | 72.5%† | – |
| VISTA | Scale AI SEAL | 45.5%† | – |
| MMMU-Pro (Vals) | Vals AI | 83.9%† | – |
| MortgageTax | Vals AI | 68.5%† | – |
| VTB | Scale AI SEAL | 27.5%† | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 67.0%† | 75.7% |
| Arena Document | Arena (LMArena) | 1495 | – |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 155.7† | 145.8† |
| LiveBench 1.0 | LiveBench | 74.5% | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 26.4† | 27.9 |
| AA Progress Index v1 | Artificial Analysis | 274.5† | 280.9 |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | 20.5%† | – |
| MASK | Scale AI SEAL | 96.3%† | – |
Data as of 2026-10-29. Best configuration of each model; every score links to its source on the model pages.
What changed
- GLM-5 (thinking): BenchLeader Index 60.6 → 60.1.
- GLM-5 (thinking): 1 new result (AA Progress Index v1); cached input price moved from $0.15/M to $0.20/M (34% up).
- GLM-5 (thinking): cached input price moved from $0.20/M to $0.15/M (25% down).
- GLM-5 (thinking): cached input price moved from $0.15/M to $0.20/M (34% up).
- GLM-5 (thinking): cached input price moved from $0.20/M to $0.15/M (25% down).
- GLM-5 (thinking): cached input price moved from $0.15/M to $0.20/M (34% up).
- GLM-5 (thinking): cached input price moved from $0.20/M to $0.15/M (25% down).
- Claude Opus 4.6 (high): 1 new result (USAMO 2026).
- GLM-5 (thinking): 1 new result (τ³-bench Banking).
- Claude Opus 4.6 (high): BenchLeader Index 61.3 → 63.0.
Related comparisons
- Claude Opus 4.6 vs Claude Opus 5.5
- Claude Opus 4.6 vs Gemini 4 Carbon
- Claude Opus 4.6 vs GPT-6 Astra
- GLM-5 vs Claude Opus 5.5
- GLM-5 vs Gemini 4 Carbon
- GLM-5 vs GPT-6 Astra
- GLM-5 vs GPT-6.1 Sol
- Claude Opus 4.6 vs Claude Fable 5.1
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
Claude Opus 4.6 vs GLM-5: questions
- Is Claude Opus 4.6 better than GLM-5?
- Claude Opus 4.6 (high) and GLM-5 (thinking) are neck and neck on quality (62.6 vs 60.2, within the ±4.3 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 29 Oct 2026, so check the category scores for your use.
- Claude Opus 4.6 vs GLM-5: which is cheaper, and will that change?
- GLM-5 is cheaper today: $1.55 against $10.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Claude Opus 4.6 or GLM-5 better for coding?
- Claude Opus 4.6 scores higher in coding (63 vs 59 on the category index, where 50 is average).
- Which is faster, Claude Opus 4.6 or GLM-5?
- GLM-5 streams ~1.5× faster (69 vs 35 output tokens per second, Artificial Analysis 7-day median).
- Which is better for long documents?
- Claude Opus 4.6 accepts more context: 1,000,000 against 204,800 tokens for GLM-5.
More questions (2)
- Which has lower latency?
- Claude Opus 4.6 answers first: 1.2 s against 46.9 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- When was GLM-5 released?
- GLM-5 was released on 11 Feb 2026.