GLM-5 vs GPT-6 Astra
Full profiles: GLM-5 (Zhipu AI) and GPT-6 Astra (OpenAI): every benchmark, price and rank.
Updated · Data from 11 sources
As of 29 Oct 2026, GPT-6 Astra is ahead of GLM-5 on quality (68.2 vs 60.2 on the BenchLeader Index), but GLM-5 is 13× cheaper at $1.55 vs $20.00 per 1M tokens blended. Choose GLM-5 for knowledge (57 vs 56), where it also streams ~2× faster; choose GPT-6 Astra for fast first answers (3.0 s vs 46.8 s) and coding (83 vs 59), reasoning (86 vs 68), and maths (78 vs 63).
Key facts (as of 29 Oct 2026)
- GLM-5 scores 60.2 on the BenchLeader Index and GPT-6 Astra scores 68.2 (BenchLeader, 29 Oct 2026).
- GLM-5 costs $1.55 per 1M tokens blended (list price, 29 Oct 2026).
- GPT-6 Astra costs $20.00 per 1M tokens blended (list price, 29 Oct 2026).
- GLM-5 streams ~2× faster than GPT-6 Astra (Artificial Analysis, 7-day median, 29 Oct 2026).
GLM-5 benchmarks 
GPT-6 Astra benchmarks Which 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 | GPT-6 Astra | 83 vs 59 category score | High |
| Agents & tool use | GPT-6 Astra | 70 vs 58 category score | High |
| Maths | GPT-6 Astra | 78 vs 63 category score | High |
| Reasoning | GPT-6 Astra | 86 vs 68 category score | High |
| Knowledge | GLM-5 | 57 vs 56 category score | Medium |
| Lowest cost | GLM-5 | $1.55 vs $20.00 per 1M blended; agentic coding session $0.073 vs $0.800 | – |
| Fastest first answer | GPT-6 Astra | 3.0 s (GPT-6 Astra) vs 46.8 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
- GPT-6 Astra leads on quality: 68.2 vs 60.2.
- GLM-5 (thinking) is stronger in instruction following, knowledge, long context.
- GPT-6 Astra is stronger in agents & tools, coding, maths, reasoning, composite.
- GLM-5 (thinking) is 13× cheaper ($1.55 vs $20.00 per 1M blended).
- GLM-5 (thinking) streams 1.7× faster (69 vs 40 tokens per second).
At a glance
| Spec | GLM-5 | GPT-6 Astra |
|---|---|---|
| Developer | Zhipu AI | OpenAI |
| Released | 11 Feb 2026 | 2 Sept 2026 |
| Context window (tokens) | 204,800 | 1,050,000 |
| Compared at | GLM-5 (thinking) | GPT-6 Astra |
| Open weights | Yes | No |
| Price in / out / cached input per 1M | $1.00 / $3.20 / $0.149 | $10.00 / $50.00 / $1.00 |
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 | GLM-5 (thinking) | GPT-6 Astra | Leader |
|---|---|---|---|
| Reasoning | 68.2 | 85.6 | GPT-6 Astra |
| Coding | 59.1 | 83.2 | GPT-6 Astra |
| Agents & tools | 58.0 | 69.6 | GPT-6 Astra |
| Maths | 63.2 | 77.5 | GPT-6 Astra |
| Knowledge | 57.3 | 55.7 | GLM-5 |
| Instruction following | 70.8 | – | not comparable |
| Long context | 62.6 | – | not comparable |
| Composite | – | 73.7 | not comparable |
Where sources disagree
Across the 0 benchmarks both have, GLM-5 leads 0, GPT-6 Astra leads 0. Every overall index that rates both puts GPT-6 Astra ahead.
| Overall index | GLM-5 (thinking) | GPT-6 Astra | Leader |
|---|---|---|---|
| BenchLeader Index | 60.2 | 68.2 | GPT-6 Astra |
| Arena Text | 1457† | 1477 | GPT-6 Astra |
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 | GLM-5 (thinking) | GPT-6 Astra |
|---|---|---|
| Blended price per 1M tokens | $1.55 | $20.00 |
| Input / output per 1M | $1.00 / $3.20 | $10.00 / $50.00 |
| Cached input per 1M | $0.149 | $1.00 |
| Price per task: chat reply | $0.0014 | $0.019 |
| Price per task: summarize a 30-page report | $0.014 | $0.150 |
| Price per task: code edit | $0.011 | $0.135 |
| Price per task: agentic coding session | $0.073 | $0.800 |
| Price per task: structured extraction | $0.0026 | $0.030 |
| Output speed | 69 tok/s | 40 tok/s |
| Time to first answer | 46.8 s | 3.0 s |
| Time to a complete answer | 54.0 s | – |
Each model is compared at its best-ranked reasoning-effort setting. GLM-5 (thinking) scores 60.2 and takes 47 s to its first answer token; GLM-5 scores 54.9 in 2 s. GPT-6 Astra scores 68.2 and takes 3 s to its first answer token; GPT-6 Astra (max) scores 67.7 in 232 s, GPT-6 Astra (high) scores 66.8 in 33 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
165 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 | GLM-5 (thinking) | GPT-6 Astra |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 60.2 | 68.2 |
| Agents & tools score | BenchLeader | 58.0 | 69.6 |
| Coding score | BenchLeader | 59.1 | 83.2 |
| Instruction following score | BenchLeader | 70.8 | – |
| Knowledge score | BenchLeader | 57.3 | 55.7 |
| Long context score | BenchLeader | 62.6 | – |
| Maths score | BenchLeader | 63.2 | 77.5 |
| Reasoning score | BenchLeader | 68.2 | 85.6 |
| Composite score | BenchLeader | – | 73.7 |
| Blended price $/M | BenchLeader | $1.55 | $20.00 |
| Output speed | BenchLeader | 69 tok/s | 40 tok/s |
| Time to first answer | BenchLeader | 46.8 s | 3.0 s |
| Context window | BenchLeader | 204,800 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 87.8%† | 95.8%† |
| Humanity's Last Exam | Scale AI / CAIS | – | 54.8% |
| SimpleBench | SimpleBench | 53.2%† | – |
| Arena Hard Prompts | Arena (LMArena) | 1478† | 1501† |
| LiveBench 2.0 Reasoning | LiveBench | – | 92.7% |
| LiveBench 1.0 Reasoning | LiveBench | – | 92.7%† |
| GPQA Diamond (AA) | Artificial Analysis | 82.0% | 96.1%† |
| Humanity's Last Exam (AA) | Artificial Analysis | 29.3% | 54.7%† |
| HLE-Diamond (AA) | Artificial Analysis | – | 63.8%† |
| GPQA Diamond (Vals) | Vals AI | 83.3% | – |
| Kagi LLM Benchmark | Kagi LLM Benchmark | 75.0% | – |
| ARC-AGI-1 | ARC Prize | 44.7%† | 97.5%† |
| ARC-AGI-2 | ARC Prize | 4.9%† | 95.0%† |
| ARC-AGI-3 | ARC Prize | – | 98.6%† |
| CritPt | Artificial Analysis | 2.0% | 31.7%† |
| CritPt v2 hard subset (AA) | Artificial Analysis | – | 70.5%† |
| MysteryMechanism | Vals AI | – | 53.1%† |
| Chess Puzzles | Epoch AI Benchmarking Hub | – | 74.0%† |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | – | 84.0%† |
| CL-bench | Epoch AI Benchmarking Hub | 18.7%† | – |
| LMCA | Epoch AI Benchmarking Hub | – | 64.1% |
| DTBench | Epoch AI Benchmarking Hub | – | 97.1% |
| ForecastBench | ForecastBench | 63.7%† | – |
| BTF-3 | Epoch AI Benchmarking Hub | – | 13.5%† |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | 72.1%† | – |
Show all 165 benchmarks
| Metric | Publisher | GLM-5 (thinking) | GPT-6 Astra |
|---|---|---|---|
| Coding | |||
| SciCode | SciCode | – | 56.5%† |
| WeirdML | WeirdML | 48.2%† | 93.3%† |
| FrontierCode | Cognition | – | 53.3%† |
| GSO-Bench | GSO-Bench | – | 79.4% |
| Arena Coding | Arena (LMArena) | 1498† | 1543† |
| Arena WebDev | Arena (LMArena) | 1436† | 1774† |
| LiveBench 2.0 Coding | LiveBench | – | 93.7% |
| LiveBench 1.0 Coding | LiveBench | – | 80.4%† |
| LiveCodeBench | Vals AI | 81.9% | – |
| IOI | Vals AI | – | 100.0%† |
| SWE-bench (Vals) | Vals AI | 71.4% | – |
| SWE-bench Verified (bash only) | SWE-bench | 72.8%† | – |
| SWE-bench Verified (any scaffold) | SWE-bench | 72.8%† | – |
| Code Migration | Vals AI | – | 67.7%† |
| Code Migration v1.1 | Vals AI | – | 80.6%† |
| ProgramBench | Vals AI | – | 5.5%† |
| SWE-bench Multimodal | Vals AI | – | 43.3%† |
| Vibe Code Bench 1-100 | Vals AI | – | 27.6%† |
| Vibe Code Bench v1.1 | Vals AI | 23.4% | 89.6%† |
| SWE Atlas: Codebase QnA | Scale AI SEAL | 20.5%† | 59.1%† |
| SWE Atlas: Refactoring | Scale AI SEAL | 24.2%† | 59.0%† |
| SWE Atlas: Test Writing | Scale AI SEAL | 28.7%† | 50.7%† |
| MirrorCode | Epoch AI Benchmarking Hub | – | 46.7%† |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | – | 73.2%† |
| ALE-Bench | Epoch AI Benchmarking Hub | 765.6† | 2951.3† |
| FrontierSWE | Epoch AI Benchmarking Hub | – | 65.5%† |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | – | 59.1%† |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | – | 88.4%† |
| Startrise Frontend | Startrise | – | 79.9% |
| FrontierCode 1.1 (AA) | Artificial Analysis | – | 47.0%† |
| FrontierCode 2.0 (AA) | Artificial Analysis | – | 42.4%† |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 52.4%† | 58.2%† |
| Remote Labor Index | Scale AI / CAIS | – | 20.8% |
| APEX-Agents | Mercor | – | 64.7% |
| Arena Agent | Arena (LMArena) | – | 14† |
| LiveBench Long Agentic | LiveBench | – | 77.5% |
| LiveBench Browser | LiveBench | – | 91.3% |
| LiveBench Agentic Coding | LiveBench | – | 57.3%† |
| Terminal-Bench Hard | Artificial Analysis | 43.2% | – |
| τ²-Bench Telecom (AA) | Artificial Analysis | 98.3% | – |
| Terminal-Bench 2.1 (Vals) | Vals AI | – | 87.3%† |
| HiL-Bench | Scale AI SEAL | – | 65.3% |
| τ²-bench | τ²-bench | 81.0% | – |
| τ³-bench Banking | τ²-bench | 9.8% | – |
| GDPval-AA v2.1, normalised score | Artificial Analysis | – | 53.7%† |
| GDPval-AA v2.1 | Artificial Analysis | – | 1584† |
| τ³-Banking (AA) | Artificial Analysis | – | 41.4%† |
| ITBench SRE (AA) | Artificial Analysis | – | 48.6%† |
| Analyst Agent (AA) | Artificial Analysis | – | 51.3%† |
| APEX-Agents (AA) | Artificial Analysis | 14.4% | – |
| CUA-bench | Vals AI | – | 19.2%† |
| CyberBench | Vals AI | – | 41.1%† |
| Finance Agent v2 | Vals AI | – | 53.5%† |
| Finance Agent v2.1 | Vals AI | – | 60.4%† |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | – | 7.1%† |
| Harvey's Legal Agent Benchmark v1 | Vals AI | – | 5.4%† |
| Legal Research Bench | Vals AI | – | 39.4%† |
| Robo-bench | Vals AI | – | 23.3%† |
| SREBench | Vals AI | – | 56.9%† |
| Tax Agent Bench | Vals AI | – | 20.7%† |
| Terminal-Bench 2.0 (Vals) | Vals AI | 49.4% | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | – | 59.6%† |
| Terminal-Bench Science | Vals AI | – | 62.9%† |
| Time Horizon Index: KSP | Vals AI | – | 90.5%† |
| Arena Search | Arena (LMArena) | – | 1246† |
| BALROG | BALROG | – | 68.3%† |
| Vending-Bench 2 | Andon Labs | 4432.1† | 15514.7 |
| AutomationBench | Artificial Analysis | – | 68.5%† |
| GDP.pdf (AA) | Artificial Analysis | – | 31.0%† |
| Harvey LAB-AA v1.1 | Harvey | – | 8.6%† |
| AA-Briefcase v1.1 | Artificial Analysis | – | 1568† |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | – | 63.3%† |
| CWE-Bench (AA) | Artificial Analysis | – | 63.3%† |
| DeepSec-Bench (AA) | Artificial Analysis | – | 36.9%† |
| CyberGym end-to-end (AA) | Artificial Analysis | – | 0.0%† |
| FACTS Search | – | 89.9% | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | – | 93.7%† |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | – | 97.6%† |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 80.0%† | 100.0%† |
| ProofBench | Vals AI | – | 99.0% |
| LiveBench Mathematics | LiveBench | – | 96.8%† |
| AIME (Vals) | Vals AI | 91.7% | – |
| AIME 2026 | MathArena | 96.7%† | – |
| HMMT February 2026 | MathArena | 86.4%† | – |
| USAMO 2026 | MathArena | 35.1%† | – |
| MathArena Apex | MathArena | 10.9%† | – |
| Arena Maths | Arena (LMArena) | 1443† | 1487† |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | – | 75.6%† |
| LiveBench 2.0 Data Analysis | LiveBench | – | 83.0% |
| LiveBench Research | LiveBench | – | 85.2% |
| LiveBench 1.0 Data Analysis | LiveBench | – | 83.0%† |
| AA-Omniscience v1.1 | Artificial Analysis | – | 42.4† |
| MMLU-Pro | Vals AI | 86.0% | – |
| LegalBench | Vals AI | 84.1% | – |
| CorpFin | Vals AI | 62.9% | – |
| TaxEval | Vals AI | 70.0% | – |
| MedQA | Vals AI | 94.3% | – |
| PRBench Finance | Scale AI SEAL | – | 47.5% |
| PRBench Legal | Scale AI SEAL | – | 48.4% |
| BioMysteryBench | Vals AI | – | 79.3%† |
| CaseLaw v2 | Vals AI | 52.5% | – |
| Excel Modeling Benchmark | Vals AI | – | 71.7%† |
| Excel Modeling Benchmark v1.1 | Vals AI | – | 72.7%† |
| MedCode | Vals AI | – | 48.5%† |
| MedScribe | Vals AI | – | 87.9%† |
| DrugDiscoveryBench | Scale AI SEAL | – | 68.7%† |
| EBR-bench | Epoch AI Benchmarking Hub | – | 76.2%† |
| GDP.pdf | Epoch AI Benchmarking Hub | – | 34.2%† |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | – | 61.6%† |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | – | 50.2%† |
| HealthBench Professional (AA) | OpenAI | – | 66.0%† |
| Instruction following | |||
| LiveBench Language | LiveBench | – | 89.4%† |
| LiveBench Instruction Following | LiveBench | – | 75.6%† |
| IFBench | Artificial Analysis | 72.3% | – |
| Arena Instruction Following | Arena (LMArena) | 1446† | 1473† |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1457† | 1477† |
| Arena Creative Writing | Arena (LMArena) | 1448† | 1450† |
| Arena Multi-turn | Arena (LMArena) | 1472† | 1485† |
| Arena Longer Queries | Arena (LMArena) | 1468† | 1489† |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | – | 1280† |
| LiveBench Design | LiveBench | – | 82.0% |
| MMMU-Pro | Artificial Analysis | – | 86.9%† |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | – | 49.7% |
| Code2Video-AA | Artificial Analysis | – | 1427† |
| AA-3D | Artificial Analysis | – | 1164† |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 75.7% | 80.7%† |
| Arena Document | Arena (LMArena) | – | 1473† |
| MLCR | Artificial Analysis | – | 41.7%† |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 145.8† | 168.8 |
| LiveBench 2.0 | LiveBench | – | 85.6% |
| LiveBench 1.0 | LiveBench | – | 82.2%† |
| AA Intelligence Index v5.0 | Artificial Analysis | – | 56.9† |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 27.9 | 52.7† |
| AA Progress Index v1 | Artificial Analysis | 280.9 | 349.6† |
| Vals Index | Vals AI | – | 65.0† |
| Vals Finance Index | Vals AI | – | 55.6%† |
| Vals RSI Index | Vals AI | – | 27.1%† |
| Vals Science Index | Vals AI | – | 65.1%† |
| Vals Router Index | Vals AI | – | 65.0% |
| AA Cyber Index | Artificial Analysis | – | 33.4† |
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): cached input price moved from $0.20/M to $0.15/M (25% down).
- 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).
- GPT-6 Astra: 3 score revisions.
- 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).
- GPT-6 Astra: 1 new result (Vals Router Index).
- GPT-6 Astra: 1 score revision.
Related comparisons
- GLM-5 vs Claude Opus 5.5
- GLM-5 vs GPT-6.1 Sol
- GLM-5 vs Claude Fable 5.1
- GPT-6 Astra vs Claude Opus 5.5
- GPT-6 Astra vs GPT-6.1 Sol
- GPT-6 Astra vs Claude Fable 5.1
- GPT-6 Astra vs Claude Fable 5
- GLM-5 vs GPT-5.6 Sol
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
GLM-5 vs GPT-6 Astra: questions
- Is GPT-6 Astra better than GLM-5?
- GPT-6 Astra leads on quality: 68.2 vs 60.2. The BenchLeader Index combines every independent quality benchmark; GPT-6 Astra is ahead overall as of 29 Oct 2026, but check the category scores for your use.
- GLM-5 vs GPT-6 Astra: which is cheaper, and will that change?
- GLM-5 is cheaper today: $1.55 against $20.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is GLM-5 or GPT-6 Astra better for coding?
- GPT-6 Astra scores higher in coding (83 vs 59 on the category index, where 50 is average).
- Which is faster, GLM-5 or GPT-6 Astra?
- GLM-5 streams ~2× faster (69 vs 40 output tokens per second, Artificial Analysis 7-day median).
- Is GLM-5 or GPT-6 Astra better for agents and tool use?
- GPT-6 Astra scores higher in agentic tasks (70 vs 58 on the category index, where 50 is average).
More questions (3)
- Which is better for long documents?
- GPT-6 Astra accepts more context: 1,050,000 against 204,800 tokens for GLM-5.
- Which has lower latency?
- GPT-6 Astra answers first: 3.0 s against 46.8 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 GPT-6 Astra released?
- GPT-6 Astra was released on 2 Sept 2026.