GLM-5 vs Muse Wonder
Full profiles: GLM-5 (Zhipu AI) and Muse Wonder (Meta): every benchmark, price and rank.
Updated · Data from 12 sources
As of 2 Nov 2026, GLM-5 and Muse Wonder are neck and neck on quality (60.2 vs 64.0 on the BenchLeader Index, inside the ±6.2 margin), and GLM-5 is 5.2× cheaper at $1.55 vs $8.00 per 1M tokens blended. Muse Wonder leads in every category we measure both in.
Key facts (as of 2 Nov 2026)
- GLM-5 scores 60.2 on the BenchLeader Index and Muse Wonder scores 64.0 (BenchLeader, 2 Nov 2026).
- GLM-5 costs $1.55 per 1M tokens blended (list price, 2 Nov 2026).
- Muse Wonder costs $8.00 per 1M tokens blended (list price, 2 Nov 2026).
GLM-5 benchmarks 
Muse Wonder 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 |
|---|---|---|---|
| Agents & tool use | Muse Wonder | 79 vs 58 category score | High |
| Maths | Muse Wonder | 70 vs 63 category score | Medium |
| Knowledge | Muse Wonder | 76 vs 57 category score | High |
| Chat quality (human preference) | – | Only Muse Wonder has an Arena rating (70 category score) | Not comparable |
| Lowest cost | GLM-5 | $1.55 vs $8.00 per 1M blended; agentic coding session $0.073 vs $0.320 | – |
| Fastest first answer | Muse Wonder | 2.7 s (Muse Wonder) vs 51.4 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
- GLM-5 (thinking) and Muse Wonder are neck and neck on quality (60.2 vs 64.0, within the ±6.2 margin).
- GLM-5 (thinking) is stronger in coding, instruction following, long context, reasoning.
- Muse Wonder is stronger in agents & tools, knowledge, maths, composite, human preference.
- GLM-5 (thinking) is 5.2× cheaper ($1.55 vs $8.00 per 1M blended).
- Muse Wonder streams 1.3× faster (84 vs 62 tokens per second).
At a glance
| Spec | GLM-5 | Muse Wonder |
|---|---|---|
| Developer | Zhipu AI | Meta |
| Released | 11 Feb 2026 | 12 Oct 2026 |
| Context window (tokens) | 204,800 | 1,048,576 |
| Compared at | GLM-5 (thinking) | Muse Wonder |
| Open weights | Yes | No |
| Price in / out / cached input per 1M | $1.00 / $3.20 / $0.200 | $4.00 / $20.00 / $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 | GLM-5 (thinking) | Muse Wonder | Leader |
|---|---|---|---|
| Reasoning | 68.2 | – | not comparable |
| Coding | 59.1 | – | not comparable |
| Agents & tools | 58.0 | 78.7 | Muse Wonder |
| Maths | 63.2 | 70.3 | Muse Wonder |
| Knowledge | 57.3 | 76.4 | Muse Wonder |
| Instruction following | 70.8 | – | not comparable |
| Human preference | – | 69.6 | not comparable |
| Long context | 62.6 | – | not comparable |
| Composite | – | 60.0 | not comparable |
On one chart
Each domain on the index's own scale, where 50 is an average model. Click a domain to see the benchmarks behind it. Pick up to 5 models from the list.
Not drawn, measured for fewer than two of these models: Reasoning, Coding, Instruction following, Long context, Multimodal, Human preference.
Where sources disagree
Across the 0 benchmarks both have, GLM-5 leads 0, Muse Wonder leads 0. Every overall index that rates both puts Muse Wonder ahead.
| Overall index | GLM-5 (thinking) | Muse Wonder | Leader |
|---|---|---|---|
| BenchLeader Index | 60.2 | 64.0 | neck and neck |
| Arena Text | 1457† | 1507 | Muse Wonder |
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. Cost, tokens and time per benchmark task are measured: Artificial Analysis’ averages over its whole Intelligence Index run.
| Measure | GLM-5 (thinking) | Muse Wonder |
|---|---|---|
| Blended price per 1M tokens | $1.55 | $8.00 |
| Input / output per 1M | $1.00 / $3.20 | $4.00 / $20.00 |
| Cached input per 1M | $0.200 | $0.200 |
| Price per task: chat reply | $0.0014 | $0.0076 |
| Price per task: summarize a 30-page report | $0.014 | $0.060 |
| Price per task: code edit | $0.011 | $0.054 |
| Price per task: agentic coding session | $0.073 | $0.320 |
| Price per task: structured extraction | $0.0026 | $0.012 |
| Agentic coding session, input mostly cached | $0.037 | $0.149 |
| Output speed | 62 tok/s | 84 tok/s |
| Time to first answer | 51.4 s | 2.7 s |
| Time to a complete answer | 59.4 s | – |
Each model is compared at its best-ranked reasoning-effort setting. GLM-5 (thinking) scores 60.2 and takes 51 s to its first answer token; GLM-5 scores 54.9 in 2 s. Muse Wonder scores 64.0 and takes 3 s to its first answer token; Muse Wonder (max) scores 62.2 in 37 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
134 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) | Muse Wonder |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 60.2 | 64.0 |
| Agents & tools score | BenchLeader | 58.0 | 78.7 |
| Coding score | BenchLeader | 59.1 | – |
| Instruction following score | BenchLeader | 70.8 | – |
| Knowledge score | BenchLeader | 57.3 | 76.4 |
| Long context score | BenchLeader | 62.6 | – |
| Maths score | BenchLeader | 63.2 | 70.3 |
| Reasoning score | BenchLeader | 68.2 | – |
| Composite score | BenchLeader | – | 60.0 |
| Human preference score | BenchLeader | – | 69.6 |
| Blended price $/M | BenchLeader | $1.55 | $8.00 |
| Output speed | BenchLeader | 62 tok/s | 84 tok/s |
| Time to first answer | BenchLeader | 51.4 s | 2.7 s |
| Context window | BenchLeader | 204,800 tokens | 1,048,576 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 87.8%† | – |
| SimpleBench | SimpleBench | 53.2%† | – |
| Arena Hard Prompts | Arena (LMArena) | 1478† | 1524† |
| LiveBench 2.0 Reasoning | LiveBench | – | 90.6% |
| LiveBench 1.0 Reasoning | LiveBench | – | 90.6%† |
| GPQA Diamond (AA) | Artificial Analysis | 82.0% | – |
| Humanity's Last Exam (AA) | Artificial Analysis | 29.3% | 50.1%† |
| HLE-Diamond (AA) | Artificial Analysis | – | 39.0%† |
| GPQA Diamond (Vals) | Vals AI | 83.3% | – |
| Kagi LLM Benchmark | Kagi LLM Benchmark | 75.0% | – |
| ARC-AGI-1 | ARC Prize | 44.7%† | – |
| ARC-AGI-2 | ARC Prize | 4.9%† | – |
| CritPt | Artificial Analysis | 2.0% | 28.3%† |
| CritPt v2 hard subset (AA) | Artificial Analysis | – | 48.0%† |
| MysteryMechanism | Vals AI | – | 27.5%† |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | – | 51.0%† |
| CL-bench | Epoch AI Benchmarking Hub | 18.7%† | – |
| LMCA | Epoch AI Benchmarking Hub | – | 56.8% |
| DTBench | Epoch AI Benchmarking Hub | – | 95.2% |
| ForecastBench | ForecastBench | 63.7%† | – |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | 72.1%† | – |
| SciCode | SciCode | – | 60.3%† |
| WeirdML | WeirdML | 48.2%† | – |
| Arena Coding | Arena (LMArena) | 1498† | 1545† |
| Arena WebDev | Arena (LMArena) | 1436† | 1652† |
Show all 134 benchmarks
| Metric | Publisher | GLM-5 (thinking) | Muse Wonder |
|---|---|---|---|
| Coding | |||
| LiveBench 2.0 Coding | LiveBench | – | 86.2% |
| LiveBench 1.0 Coding | LiveBench | – | 80.7%† |
| LiveCodeBench | Vals AI | 81.9% | – |
| IOI | Vals AI | – | 66.9%† |
| 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 | – | 62.5%† |
| Code Migration v1.1 | Vals AI | – | 72.4%† |
| SWE-bench Multimodal | Vals AI | – | 45.6%† |
| Vibe Code Bench 1-100 | Vals AI | – | 26.6%† |
| Vibe Code Bench v1.1 | Vals AI | 23.4% | 83.1%† |
| SWE Atlas: Codebase QnA | Scale AI SEAL | 20.5%† | – |
| SWE Atlas: Refactoring | Scale AI SEAL | 24.2%† | – |
| SWE Atlas: Test Writing | Scale AI SEAL | 28.7%† | – |
| CursorBench | Cursor | – | 50.4%† |
| ALE-Bench | Epoch AI Benchmarking Hub | 765.6† | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | – | 52.0%† |
| Startrise Frontend | Startrise | – | 75.3% |
| GameBench 2 Overall | DuelLab GameBench 2 | – | 66.4 |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | – | 56.8† |
| FrontierCode 1.1 (AA) | Artificial Analysis | – | 46.5%† |
| FrontierCode 2.0 (AA) | Artificial Analysis | – | 27.9%† |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 52.4%† | – |
| APEX-Agents | Mercor | – | 66.1%† |
| Arena Agent | Arena (LMArena) | – | 3.1† |
| LiveBench Long Agentic | LiveBench | – | 52.6% |
| LiveBench Browser | LiveBench | – | 60.0% |
| LiveBench Agentic Coding | LiveBench | – | 62.7%† |
| Terminal-Bench Hard | Artificial Analysis | 43.2% | – |
| τ²-Bench Telecom (AA) | Artificial Analysis | 98.3% | – |
| HiL-Bench | Scale AI SEAL | – | 79.7% |
| τ²-bench | τ²-bench | 81.0% | – |
| τ³-bench Banking | τ²-bench | 9.8% | – |
| GDPval-AA v2.1, normalised score | Artificial Analysis | – | 63.5%† |
| GDPval-AA v2.1 | Artificial Analysis | – | 1770† |
| APEX-Agents (AA) | Artificial Analysis | 14.4% | – |
| CUA-bench | Vals AI | – | 7.2%† |
| CyberBench | Vals AI | – | 80.4%† |
| Finance Agent v2 | Vals AI | – | 56.6%† |
| Finance Agent v2.1 | Vals AI | – | 65.5%† |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | – | 23.8%† |
| Harvey's Legal Agent Benchmark v1 | Vals AI | – | 17.1%† |
| Legal Research Bench | Vals AI | – | 50.5%† |
| Tax Agent Bench | Vals AI | – | 36.3%† |
| Terminal-Bench 2.0 (Vals) | Vals AI | 49.4% | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | – | 49.5%† |
| Terminal-Bench Science | Vals AI | – | 22.9%† |
| Vending-Bench 2 | Andon Labs | 4432.1† | 9432.2 |
| AutomationBench | Artificial Analysis | – | 68.1%† |
| GDP.pdf (AA) | Artificial Analysis | – | 30.6%† |
| Harvey LAB-AA v1.1 | Harvey | – | 10.0%† |
| AA-Briefcase v1.1 | Artificial Analysis | – | 1659† |
| GDPx | GDPx by SemiAnalysis | – | 6.8%† |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | – | 25.7%† |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | – | 82.1%† |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | – | 70.7%† |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 80.0%† | 99.4%† |
| ProofBench | Vals AI | – | 84.0% |
| LiveBench Mathematics | LiveBench | – | 96.5%† |
| 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) | 1444† | 1489† |
| Knowledge | |||
| LiveBench 2.0 Data Analysis | LiveBench | – | 81.6% |
| LiveBench Research | LiveBench | – | 88.0% |
| LiveBench 1.0 Data Analysis | LiveBench | – | 81.6%† |
| AA-Omniscience v1.1 | Artificial Analysis | – | 25.1† |
| 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 | – | 58.7% |
| PRBench Legal | Scale AI SEAL | – | 58.4% |
| BioMysteryBench | Vals AI | – | 67.0%† |
| CaseLaw v2 | Vals AI | 52.5% | – |
| Excel Modeling Benchmark | Vals AI | – | 67.1%† |
| Excel Modeling Benchmark v1.1 | Vals AI | – | 72.5%† |
| Public Benefits Bench v1.1 | Vals AI | – | 70.2%† |
| EBR-bench | Epoch AI Benchmarking Hub | – | 31.4%† |
| GDP.pdf | Epoch AI Benchmarking Hub | – | 30.8%† |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | – | 38.0%† |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | – | 79.1%† |
| Instruction following | |||
| LiveBench Language | LiveBench | – | 82.9%† |
| LiveBench Instruction Following | LiveBench | – | 73.0%† |
| IFBench | Artificial Analysis | 72.3% | – |
| Arena Instruction Following | Arena (LMArena) | 1447† | 1498† |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1457† | 1507† |
| EQ-Bench 4 | EQ-Bench | – | 1325 |
| Arena Creative Writing | Arena (LMArena) | 1448† | 1487† |
| Arena Multi-turn | Arena (LMArena) | 1471† | 1517† |
| Arena Longer Queries | Arena (LMArena) | 1468† | 1505† |
| Multimodal | |||
| LiveBench Design | LiveBench | – | 75.1% |
| Code2Video-AA | Artificial Analysis | – | 997† |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 75.7% | 80.7%† |
| MLCR | Artificial Analysis | – | 22.2%† |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 145.8† | 161.7 |
| LiveBench 2.0 | LiveBench | – | 74.2% |
| LiveBench 1.0 | LiveBench | – | 81.2%† |
| AA Intelligence Index v5.0 | Artificial Analysis | – | 43.3† |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 27.9 | 52.4† |
| AA Progress Index v1 | Artificial Analysis | 280.9 | 335.8† |
| Vals Index | Vals AI | – | 64.9† |
| Vals Finance Index | Vals AI | – | 61.2%† |
| Vals Science Index | Vals AI | – | 39.1%† |
| Vals Router Index | Vals AI | – | 64.9% |
Data as of 2026-11-02. Best configuration of each model; every score links to its source on the model pages.
DuelLab GameBench 2, release gb2-20261026-cb69502a12d9 (https://benchmarks.duellab.org/releases/gb2-20261026-cb69502a12d9/), CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Scores mapped to BenchLeader model names; not endorsed by DuelLab.
What changed
- 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).
- Muse Wonder: 1 new result (Startrise Frontend).
- 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).
- Muse Wonder: 3 score revisions.
- GLM-5 (thinking): cached input price moved from $0.20/M to $0.15/M (25% down).
- Muse Wonder: 1 new result (EQ-Bench 4).
Related comparisons
- GLM-5 vs Claude Opus 5.5
- GLM-5 vs Gemini 4 Carbon
- GLM-5 vs GPT-6 Astra
- Muse Wonder vs Claude Opus 5.5
- Muse Wonder vs Gemini 4 Carbon
- Muse Wonder vs GPT-6 Astra
- Muse Wonder vs GPT-6.1 Sol
- GLM-5 vs Muse Spark 1.3
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
GLM-5 vs Muse Wonder: questions
- Is GLM-5 better than Muse Wonder?
- GLM-5 (thinking) and Muse Wonder are neck and neck on quality (60.2 vs 64.0, within the ±6.2 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2 Nov 2026, so check the category scores for your use.
- GLM-5 vs Muse Wonder: which is cheaper, and will that change?
- GLM-5 is cheaper today: $1.55 against $8.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Which is faster, GLM-5 or Muse Wonder?
- Muse Wonder streams faster (84 vs 62 output tokens per second, Artificial Analysis 7-day median).
- Is GLM-5 or Muse Wonder better for agents and tool use?
- Muse Wonder scores higher in agentic tasks (79 vs 58 on the category index, where 50 is average).
- Which is better for long documents?
- Muse Wonder accepts more context: 1,048,576 against 204,800 tokens for GLM-5.
More questions (2)
- Which has lower latency?
- Muse Wonder answers first: 2.7 s against 51.4 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 Muse Wonder released?
- Muse Wonder was released on 12 Oct 2026.