GLM 5.3 vs GPT-6.1 Terra
Full profiles: GLM 5.3 (Zhipu AI) and GPT-6.1 Terra (OpenAI): every benchmark, price and rank.
Updated · Data from 8 sources
As of 27 Oct 2026, GLM 5.3 (max) and GPT-6.1 Terra (max) are neck and neck on quality (63.4 vs 65.9, inside the ±7.7 margin of error) and close on price, so pick on the category that matters to you. GPT-6.1 Terra leads in every category we measure both in.
GLM 5.3 benchmarks 
GPT-6.1 Terra 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 |
|---|---|---|---|
| Reasoning | GPT-6.1 Terra | 86 vs 68 category score; CritPt 28.0% vs 19.1% | High |
| Knowledge | GPT-6.1 Terra | 75 vs 59 category score; AA-Omniscience 29.8 vs 14.3 | High |
| Long documents | GPT-6.1 Terra | 66 vs 65 category score; AA-LCR 82.7% vs 79.7% | Medium |
| Chat quality (human preference) | – | Only GLM 5.3 has an Arena rating (67 category score) | Not comparable |
| Lowest cost | Either | $2.15 vs $2.00 per 1M blended; agentic coding session $0.102 vs $0.080 | – |
| Fastest first answer | GLM 5.3 | 23.7 s (GLM 5.3 (max)) vs 131.8 s (GPT-6.1 Terra (max)) 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
- GLM 5.3 (max) and GPT-6.1 Terra (max) are neck and neck on quality (63.4 vs 65.9, within the ±7.7 margin).
- GLM 5.3 (max) is stronger in agents & tools, coding, human preference, maths.
- GPT-6.1 Terra (max) is stronger in composite, knowledge, long context, reasoning, multimodal.
- They cost about the same ($2.00 per 1M blended).
At a glance
| Spec | GLM 5.3 | GPT-6.1 Terra |
|---|---|---|
| Developer | Zhipu AI | OpenAI |
| Released | 18 Aug 2026 | 26 Oct 2026 |
| Context window (tokens) | 1,000,000 | 1,050,000 |
| Compared at | GLM 5.3 (max) | GPT-6.1 Terra (max) |
| Open weights | Yes | No |
| Price in / out / cached input per 1M | $1.40 / $4.40 / $0.299 | $1.00 / $5.00 / $0.050 |
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 5.3 (max) | GPT-6.1 Terra (max) | Leader |
|---|---|---|---|
| Reasoning | 68.3 | 86.2 | GPT-6.1 Terra |
| Coding | 61.9 | – | not comparable |
| Agents & tools | 57.9 | – | not comparable |
| Maths | 60.4 | – | not comparable |
| Knowledge | 58.5 | 74.5 | GPT-6.1 Terra |
| Human preference | 67.3 | – | not comparable |
| Multimodal | – | 67.5 | not comparable |
| Long context | 64.7 | 66.3 | GPT-6.1 Terra |
| Composite | 79.1 | 82.8 | GPT-6.1 Terra |
Where sources disagree
Across the 13 benchmarks both have, GLM 5.3 leads 5, GPT-6.1 Terra leads 8. Every overall index that rates both puts GPT-6.1 Terra ahead.
| Overall index | GLM 5.3 (max) | GPT-6.1 Terra (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 63.4 | 65.9 | neck and neck |
| AA Intelligence Index v4.3.2 | 44.8 | 48.1 | GPT-6.1 Terra |
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 5.3 (max) | GPT-6.1 Terra (max) |
|---|---|---|
| Blended price per 1M tokens | $2.15 | $2.00 |
| Input / output per 1M | $1.40 / $4.40 | $1.00 / $5.00 |
| Cached input per 1M | $0.299 | $0.050 |
| Price per task: chat reply | $0.0019 | $0.0019 |
| Price per task: summarize a 30-page report | $0.019 | $0.015 |
| Price per task: code edit | $0.015 | $0.013 |
| Price per task: agentic coding session | $0.102 | $0.080 |
| Price per task: structured extraction | $0.0037 | $0.0030 |
| Cost to run a full benchmark suite | $2.01 | $0.403 |
| Output speed | 94 tok/s | 91 tok/s |
| Time to first answer | 23.7 s | 131.8 s |
| Time to a complete answer | 29.1 s | 137.3 s |
Per token is not per task: on Artificial Analysis' full suite GLM 5.3 (max) cost $2.01 and GPT-6.1 Terra (max) $0.403, because GLM 5.3 (max) used about 4.6× as many tokens for the same work, which a per-token price hides.
Each model is compared at its best-ranked reasoning-effort setting. GLM 5.3 (max) scores 63.4 and takes 24 s to its first answer token; GLM 5.3 (low) scores 55.4 in 25 s. GPT-6.1 Terra (max) scores 65.9 and takes 132 s to its first answer token; GPT-6.1 Terra (xhigh) scores 65.0 in 18 s, GPT-6.1 Terra (high) scores 64.7 in 7 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
116 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 5.3 (max) | GPT-6.1 Terra (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 63.4 | 65.9 |
| Agents & tools score | BenchLeader | 57.9 | – |
| Coding score | BenchLeader | 61.9 | – |
| Composite score | BenchLeader | 79.1 | 82.8 |
| Human preference score | BenchLeader | 67.3 | – |
| Knowledge score | BenchLeader | 58.5 | 74.5 |
| Long context score | BenchLeader | 64.7 | 66.3 |
| Maths score | BenchLeader | 60.4 | – |
| Reasoning score | BenchLeader | 68.3 | 86.2 |
| Multimodal score | BenchLeader | – | 67.5 |
| Blended price $/M | BenchLeader | $2.15 | $2.00 |
| Output speed | BenchLeader | 94 tok/s | 91 tok/s |
| Time to first answer | BenchLeader | 23.7 s | 131.8 s |
| Context window | BenchLeader | 1,000,000 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 90.9% | – |
| Arena Hard Prompts | Arena (LMArena) | 1505 | – |
| LiveBench 2.0 Reasoning | LiveBench | – | 88.7%† |
| LiveBench 1.0 Reasoning | LiveBench | 85.8%† | – |
| GPQA Diamond (AA) | Artificial Analysis | 91.7% | – |
| Humanity's Last Exam (AA) | Artificial Analysis | 42.3% | 46.3% |
| GPQA Diamond (Vals) | Vals AI | 88.1% | – |
| CritPt | Artificial Analysis | 19.1% | 28.0% |
| MysteryMechanism | Vals AI | 23.0% | – |
| Chess Puzzles | Epoch AI Benchmarking Hub | 37.0% | – |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 33.0% | – |
| LMCA | Epoch AI Benchmarking Hub | 55.5%† | – |
| DTBench | Epoch AI Benchmarking Hub | 87.7%† | – |
| BTF-3 | Epoch AI Benchmarking Hub | 14.9%† | – |
| Coding | |||
| SciCode | SciCode | 59.0% | – |
| WeirdML | WeirdML | 75.4% | – |
| FrontierCode | Cognition | 40.1% | – |
| Arena Coding | Arena (LMArena) | 1521 | – |
| Arena WebDev | Arena (LMArena) | 1623 | – |
| LiveBench 2.0 Coding | LiveBench | – | 90.8%† |
| LiveBench 1.0 Coding | LiveBench | 79.0%† | – |
| SciCode (AA) | Artificial Analysis | 59.0% | 55.1% |
| LiveCodeBench | Vals AI | 80.5% | – |
| IOI | Vals AI | 68.4% | – |
| SWE-bench (Vals) | Vals AI | 95.4% | – |
Show all 116 benchmarks
| Metric | Publisher | GLM 5.3 (max) | GPT-6.1 Terra (max) |
|---|---|---|---|
| Coding | |||
| Code Migration | Vals AI | 44.2% | – |
| Code Migration v1.1 | Vals AI | 55.1% | – |
| ProgramBench | Vals AI | 1.5% | – |
| Vibe Code Bench 1-100 | Vals AI | 20.0% | – |
| Vibe Code Bench v1.1 | Vals AI | 78.1% | – |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | 69.0% | – |
| CursorBench | Cursor | 42.6% | – |
| ALE-Bench | Epoch AI Benchmarking Hub | 1317.4† | – |
| FrontierSWE | Epoch AI Benchmarking Hub | 30.2% | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 41.9% | 55.6% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 83.9% | – |
| Startrise Frontend | Startrise | 73.2%† | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | 40.1% | 36.0% |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 41.8% | – |
| APEX-Agents | Mercor | 56.6%† | – |
| Arena Agent | Arena (LMArena) | 1.2 | – |
| LiveBench Long Agentic | LiveBench | – | 48.6%† |
| LiveBench Browser | LiveBench | – | 93.1%† |
| LiveBench Agentic Coding | LiveBench | 60.9%† | – |
| Terminal-Bench 2.1 (Vals) | Vals AI | 71.5% | – |
| MCP Atlas | Scale AI SEAL | 84.2%† | – |
| HiL-Bench | Scale AI SEAL | 58.3%† | – |
| GDPval-AA v2.1 | Artificial Analysis | 57.5% | – |
| τ³-Banking (AA) | Artificial Analysis | 50.3% | – |
| ITBench SRE (AA) | Artificial Analysis | 46.1% | – |
| Analyst Agent (AA) | Artificial Analysis | 38.8% | – |
| CyberBench | Vals AI | 72.1% | – |
| Finance Agent v2 | Vals AI | 55.8% | – |
| Finance Agent v2.1 | Vals AI | 65.0% | – |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 14.6% | – |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 8.3% | – |
| Legal Research Bench | Vals AI | 49.0% | – |
| SkillsBench | Vals AI | 47.5% | – |
| SREBench | Vals AI | 9.2% | – |
| Tax Agent Bench | Vals AI | 39.7% | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | 38.9% | – |
| Terminal-Bench Science | Vals AI | 5.7% | – |
| Vending-Bench 2 | Andon Labs | 8163.6† | – |
| AutomationBench | Artificial Analysis | 62.2% | 60.2% |
| GDP.pdf (AA) | Artificial Analysis | 11.2% | 28.6% |
| Harvey LAB-AA v1.1 | Harvey | 0.3% | – |
| EnterpriseOps-Gym | Artificial Analysis | 36.4% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1504 | 1402 |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 9.5% | 57.6% |
| CWE-Bench (AA) | Artificial Analysis | 55.8% | – |
| DeepSec-Bench (AA) | Artificial Analysis | 24.3% | – |
| CyberGym end-to-end (AA) | Artificial Analysis | 29.0% | – |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 68.8% | – |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 29.3% | – |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 91.1% | – |
| ProofBench | Vals AI | 49.0% | – |
| LiveBench Mathematics | LiveBench | 87.9%† | – |
| Arena Maths | Arena (LMArena) | 1500 | – |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 41.0% | – |
| LiveBench 2.0 Data Analysis | LiveBench | – | 82.3%† |
| LiveBench Research | LiveBench | – | 73.9%† |
| LiveBench 1.0 Data Analysis | LiveBench | 70.2%† | – |
| AA-Omniscience | Artificial Analysis | 14.3 | 29.8 |
| MMLU-Pro | Vals AI | 86.8% | – |
| LegalBench | Vals AI | 84.8% | – |
| TaxEval | Vals AI | 72.4% | – |
| Excel Modeling Benchmark | Vals AI | 56.3% | – |
| Excel Modeling Benchmark v1.1 | Vals AI | 69.1% | – |
| MedCode | Vals AI | 42.9% | – |
| MedScribe | Vals AI | 88.8% | – |
| Public Benefits Bench v1.1 | Vals AI | 68.5% | – |
| AA-Omniscience: accuracy | Artificial Analysis | 33.9% | 49.1% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 70.5% | 62.0% |
| Instruction following | |||
| LiveBench Language | LiveBench | 79.9%† | – |
| LiveBench Instruction Following | LiveBench | 69.3%† | – |
| Arena Instruction Following | Arena (LMArena) | 1478 | – |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1481 | – |
| Arena Creative Writing | Arena (LMArena) | 1456 | – |
| Arena Multi-turn | Arena (LMArena) | 1478 | – |
| Arena Longer Queries | Arena (LMArena) | 1486 | – |
| Multimodal | |||
| LiveBench Design | LiveBench | – | 82.3%† |
| MMMU-Pro | Artificial Analysis | – | 84.7% |
| AA-3D | Artificial Analysis | 783 | – |
| Long context | |||
| AA-LCR | Artificial Analysis | 79.7% | 82.7% |
| MLCR | Artificial Analysis | 17.2% | – |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 156.0† | – |
| LiveBench 2.0 | LiveBench | – | 77.2%† |
| LiveBench 1.0 | LiveBench | 76.1%† | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 44.8 | 48.1 |
| Vals Index | Vals AI | 59.7 | – |
| Vals Finance Index | Vals AI | 60.5% | – |
| Vals RSI Index | Vals AI | 22.9% | – |
| Vals Router Index | Vals AI | 59.7%† | – |
| AA Cyber Index | Artificial Analysis | 36.4 | – |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | 28.2% | 18.2%† |
| Metadata | |||
| AA Openness Index | Artificial Analysis | 33.3 | – |
Data as of 2026-10-27. Best configuration of each model; every score links to its source on the model pages.
What changed
- GLM 5.3 (max): cached input price moved from $0.26/M to $0.30/M (15% up).
- GLM 5.3 (max): cached input price moved from $0.30/M to $0.26/M (13% down).
- GLM 5.3 (max): cached input price moved from $0.26/M to $0.30/M (15% up).
- GLM 5.3 (max): cached input price moved from $0.30/M to $0.26/M (13% down).
- GLM 5.3 (max): 6 new results (Code Migration v1.1, Excel Modeling Benchmark v1.1, Finance Agent v2.1…).
- GLM 5.3 (max): 1 score revision.
- GLM 5.3 (max): 4 score revisions; cached input price moved from $0.26/M to $0.30/M (15% up).
- GLM 5.3 (max): 1 score revision.
- GLM 5.3 (max): 1 new result (SREBench); 2 score revisions.
- GLM 5.3 (max): 1 new result (Analyst Agent (AA)); 1 score revision.
Related comparisons
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- GLM 5.3 vs Claude Fable 5.1
- GLM 5.3 vs GPT-6 Astra
- GPT-6.1 Terra vs Claude Opus 5.5
- GPT-6.1 Terra vs Claude Fable 5.1
- GPT-6.1 Terra vs GPT-6 Astra
- GPT-6.1 Terra vs Muse Wonder Full
- GLM 5.2 vs GPT-6.1 Terra
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
GLM 5.3 vs GPT-6.1 Terra: questions
- Is GLM 5.3 better than GPT-6.1 Terra?
- GLM 5.3 (max) and GPT-6.1 Terra (max) are neck and neck on quality (63.4 vs 65.9, within the ±7.7 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-27, so check the category scores for your use.
- Which is cheaper, GLM 5.3 or GPT-6.1 Terra, and will that change?
- GPT-6.1 Terra is cheaper today: $2.00 against $2.15 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Which is better for long documents?
- GPT-6.1 Terra scores higher on long-context benchmarks (66 vs 65). Both take about 1.1M tokens: GLM 5.3 1,000,000, GPT-6.1 Terra 1,050,000.
- Which has lower latency?
- GLM 5.3 (max) answers first: 23.7 s against 131.8 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.
- Which is faster, GLM 5.3 or GPT-6.1 Terra?
- GLM 5.3 streams faster: 94 against 91 output tokens per second.
- Do independent benchmarks agree on GLM 5.3 vs GPT-6.1 Terra?
- Across the 13 benchmarks both have, GLM 5.3 leads 5, GPT-6.1 Terra leads 8. Every overall index that rates both puts GPT-6.1 Terra ahead.
- When was GPT-6.1 Terra released?
- GPT-6.1 Terra was released on 26 Oct 2026.