DeepSeek V4 Pro vs Muse Wonder Full
Full profiles: DeepSeek V4 Pro (DeepSeek) and Muse Wonder Full (Meta): every benchmark, price and rank.
Updated · Data from 8 sources
As of 24 Oct 2026, Muse Wonder Full (max) is the better pick for most workloads: it leads on quality (70.7 vs 60.8 on the BenchLeader Index). Muse Wonder Full leads in every category we measure both in.
DeepSeek V4 Pro benchmarks max DeepSeek
Muse Wonder Full benchmarks max MetaOpen 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 | Muse Wonder Full | 72 vs 58 category score; IOI 79.8% vs 51.6% | High |
| Reasoning | Muse Wonder Full | 95 vs 65 category score; CritPt 32.9% vs 18.0% | High |
| Knowledge | Muse Wonder Full | 71 vs 60 category score; AA-Omniscience 30.1 vs 0.8 | High |
| Long documents | Muse Wonder Full | 67 vs 65 category score; AA-LCR 83.7% vs 80.3% | Medium |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- Muse Wonder Full (max) leads on quality: 70.7 vs 60.8.
- DeepSeek V4 Pro (max) is stronger in agents & tools, maths.
- Muse Wonder Full (max) is stronger in coding, composite, knowledge, long context, reasoning.
At a glance
| Spec | DeepSeek V4 Pro | Muse Wonder Full |
|---|---|---|
| Developer | DeepSeek | Meta |
| Released | 13 Aug 2026 | 12 Oct 2026 |
| Context window (tokens) | 1,048,576 | 1,000,000 |
| Compared at | DeepSeek V4 Pro (max) | Muse Wonder Full (max) |
| Open weights | Yes | No |
| Price in / out / cached input per 1M | $1.32 / $3.96 / $0.022 | – |
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 | DeepSeek V4 Pro (max) | Muse Wonder Full (max) | Leader |
|---|---|---|---|
| Reasoning | 65.1 | 94.9 | Muse Wonder Full |
| Coding | 58.3 | 72.3 | Muse Wonder Full |
| Agents & tools | 60.2 | – | not comparable |
| Maths | 56.1 | – | not comparable |
| Knowledge | 59.6 | 71.4 | Muse Wonder Full |
| Long context | 65.2 | 67.0 | Muse Wonder Full |
| Composite | 69.8 | 91.9 | Muse Wonder Full |
Where sources disagree
Across the 37 benchmarks both have, DeepSeek V4 Pro leads 1, Muse Wonder Full leads 36. Every overall index that rates both puts Muse Wonder Full ahead.
| Overall index | DeepSeek V4 Pro (max) | Muse Wonder Full (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 60.8 | 70.7 | Muse Wonder Full |
| AA Intelligence Index v4.3.2 | 36 | 55.8 | Muse Wonder Full |
| Vals Index | 53.0 | 66.8 | Muse Wonder Full |
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 | DeepSeek V4 Pro (max) | Muse Wonder Full (max) |
|---|---|---|
| Blended price per 1M tokens | $1.98 | – |
| Input / output per 1M | $1.32 / $3.96 | – / – |
| Cached input per 1M | $0.022 | – |
| Price per task: chat reply | $0.0017 | – |
| Price per task: summarize a 30-page report | $0.018 | – |
| Price per task: code edit | $0.014 | – |
| Price per task: agentic coding session | $0.095 | – |
| Price per task: structured extraction | $0.0034 | – |
| Cost to run a full benchmark suite | $0.674 | – |
| Output speed | 93 tok/s | – |
| Time to first answer | 23.3 s | – |
| Time to a complete answer | 28.7 s | – |
Each model is compared at its best-ranked reasoning-effort setting. DeepSeek V4 Pro (max) scores 60.8 and takes 23 s to its first answer token; DeepSeek V4 Pro (high) scores 57.5 in 2 s, DeepSeek V4 Pro scores 53.5 in 2 s. Muse Wonder Full (max) scores 70.7. 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
114 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 | DeepSeek V4 Pro (max) | Muse Wonder Full (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 60.8 | 70.7 |
| Agents & tools score | BenchLeader | 60.2 | – |
| Coding score | BenchLeader | 58.3 | 72.3 |
| Composite score | BenchLeader | 69.8 | 91.9 |
| Knowledge score | BenchLeader | 59.6 | 71.4 |
| Long context score | BenchLeader | 65.2 | 67.0 |
| Maths score | BenchLeader | 56.1 | – |
| Reasoning score | BenchLeader | 65.1 | 94.9 |
| Blended price $/M | BenchLeader | $1.98 | – |
| Output speed | BenchLeader | 93 tok/s | – |
| Time to first answer | BenchLeader | 23.3 s | – |
| Context window | BenchLeader | 1,048,576 tokens | 1,000,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 91.7% | – |
| Arena Hard Prompts | Arena (LMArena) | 1480† | – |
| LiveBench 1.0 Reasoning | LiveBench | 85.8%† | – |
| GPQA Diamond (AA) | Artificial Analysis | 92.8% | – |
| Humanity's Last Exam (AA) | Artificial Analysis | 41.0% | 53.6% |
| GPQA Diamond (Vals) | Vals AI | 92.4% | – |
| Kagi LLM Benchmark | Kagi LLM Benchmark | 53.5%† | – |
| ARC-AGI-1 | ARC Prize | 90.0% | – |
| ARC-AGI-2 | ARC Prize | 61.3% | – |
| CritPt | Artificial Analysis | 18.0% | 32.9% |
| MysteryMechanism | Vals AI | – | 35.1% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 46.0% | – |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 43.0% | – |
| CL-bench Life | Epoch AI Benchmarking Hub | 13.5%† | – |
| LMCA | Epoch AI Benchmarking Hub | 41.2% | – |
| DTBench | Epoch AI Benchmarking Hub | 90.7% | – |
| ForecastBench | ForecastBench | 60.9%† | – |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | 77.6% | – |
| SciCode | SciCode | 51.0% | – |
| WeirdML | WeirdML | 66.2% | – |
| FrontierCode | Cognition | 28.6%† | – |
| Arena Coding | Arena (LMArena) | 1501† | – |
| Arena WebDev | Arena (LMArena) | 1447† | 1726 |
| LiveBench 1.0 Coding | LiveBench | 77.2%† | – |
| SciCode (AA) | Artificial Analysis | 51.0% | 62.6% |
Show all 114 benchmarks
| Metric | Publisher | DeepSeek V4 Pro (max) | Muse Wonder Full (max) |
|---|---|---|---|
| Coding | |||
| LiveCodeBench | Vals AI | 87.5% | – |
| IOI | Vals AI | 51.6% | 79.8% |
| SWE-bench (Vals) | Vals AI | 96.4% | – |
| Code Migration | Vals AI | 41.5% | 61.0% |
| Code Migration v1.1 | Vals AI | 52.0% | 62.8% |
| ProgramBench | Vals AI | 0.0% | 0.5% |
| SWE-bench Multimodal | Vals AI | – | 47.3% |
| Vibe Code Bench 1-100 | Vals AI | 17.5% | 34.0% |
| Vibe Code Bench v1.1 | Vals AI | 82.3% | 86.0% |
| SWE Atlas: Codebase QnA | Scale AI SEAL | 27.1%† | – |
| SWE Atlas: Test Writing | Scale AI SEAL | 27.1%† | – |
| Surface Evolver Bench | Epoch AI Benchmarking Hub | 40.0%† | – |
| ALE-Bench | Epoch AI Benchmarking Hub | 1403.2 | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 14.1% | 57.1% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 78.7% | – |
| Startrise Frontend | Startrise | 62.7%† | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | 38.8% | 44.9% |
| Agents & tools | |||
| APEX-Agents | Mercor | 47.3%† | – |
| Arena Agent | Arena (LMArena) | 0.1† | – |
| LiveBench Agentic Coding | LiveBench | 55.0%† | – |
| Terminal-Bench 2.1 (Vals) | Vals AI | 54.7% | – |
| HiL-Bench | Scale AI SEAL | 38.5%† | – |
| GDPval-AA v2.1 | Artificial Analysis | 47.5% | 67.8% |
| τ³-Banking (AA) | Artificial Analysis | 39.6% | – |
| CyberBench | Vals AI | 72.9% | 73.6% |
| Finance Agent v2 | Vals AI | 50.4% | 59.1% |
| Finance Agent v2.1 | Vals AI | 57.1% | 67.6% |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 10.8% | 23.3% |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 7.5% | 21.7% |
| Legal Research Bench | Vals AI | 40.9% | 53.4% |
| Robo-bench | Vals AI | – | 10.4% |
| SkillsBench | Vals AI | 53.8% | – |
| SREBench | Vals AI | 1.9% | 11.8% |
| Tax Agent Bench | Vals AI | 26.8% | 43.0% |
| Terminal-Bench 2.0 (Vals) | Vals AI | 56.2%† | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | 14.1% | 58.1% |
| Terminal-Bench Science | Vals AI | 4.3% | 37.1% |
| Vending-Bench 2 | Andon Labs | 3284.5† | – |
| AutomationBench | Artificial Analysis | 56.7% | 71.3% |
| GDP.pdf (AA) | Artificial Analysis | 11.4% | 32.6% |
| Harvey LAB-AA v1.1 | Harvey | – | 13.1% |
| EnterpriseOps-Gym | Artificial Analysis | 49.6% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1256 | 1729 |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 5.7% | 37.1% |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 64.6% | – |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 26.8% | – |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 98.6% | – |
| ProofBench | Vals AI | 16.0% | – |
| LiveBench Mathematics | LiveBench | 95.1%† | – |
| AIME 2026 | MathArena | 96.7% | – |
| HMMT February 2026 | MathArena | 93.9% | – |
| USAMO 2026 | MathArena | 60.7% | – |
| MathArena Apex | MathArena | 28.1% | – |
| Arena Maths | Arena (LMArena) | 1444† | – |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 52.9% | – |
| LiveBench 1.0 Data Analysis | LiveBench | 79.2%† | – |
| AA-Omniscience | Artificial Analysis | 0.8 | 30.1 |
| MMLU-Pro | Vals AI | 87.3% | – |
| LegalBench | Vals AI | 82.4% | 87.4% |
| CorpFin | Vals AI | 65.4% | – |
| TaxEval | Vals AI | 73.1% | – |
| BioMysteryBench | Vals AI | – | 73.3% |
| CaseLaw v2 | Vals AI | 59.4% | – |
| Excel Modeling Benchmark | Vals AI | 52.8% | 71.9% |
| Excel Modeling Benchmark v1.1 | Vals AI | 65.1% | 75.0% |
| MedCode | Vals AI | 42.5% | 49.9% |
| MedScribe | Vals AI | 80.2% | 89.3% |
| Public Benefits Bench v1.1 | Vals AI | 62.9% | 73.8% |
| DrugDiscoveryBench | Scale AI SEAL | 31.7%† | – |
| AA-Omniscience: accuracy | Artificial Analysis | 49.1% | 43.2% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 5.2% | 77.0% |
| Instruction following | |||
| LiveBench Language | LiveBench | 82.1%† | – |
| LiveBench Instruction Following | LiveBench | 67.7%† | – |
| Arena Instruction Following | Arena (LMArena) | 1453† | – |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1458† | – |
| EQ-Bench 4 | EQ-Bench | 1166† | – |
| Arena Creative Writing | Arena (LMArena) | 1446† | – |
| Arena Multi-turn | Arena (LMArena) | 1475† | – |
| Arena Longer Queries | Arena (LMArena) | 1472† | – |
| Long context | |||
| AA-LCR | Artificial Analysis | 80.3% | 83.7% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 155.6† | – |
| LiveBench 1.0 | LiveBench | 77.4%† | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 36 | 55.8 |
| Vals Index | Vals AI | 53.0 | 66.8 |
| Vals Finance Index | Vals AI | 52.9% | 64.5% |
| Vals RSI Index | Vals AI | – | 24.4% |
| Vals Science Index | Vals AI | – | 48.5% |
| Vals Router Index | Vals AI | 53.0%† | 66.8%† |
| Metadata | |||
| AA Openness Index | Artificial Analysis | 44.4 | – |
Data as of 2026-10-24. Best configuration of each model; every score links to its source on the model pages.
What changed
- Muse Wonder Full (max): 1 new result (Terminal-Bench-Science 0.1 (AA)).
- DeepSeek V4 Pro (max): 5 new results (Code Migration v1.1, Excel Modeling Benchmark v1.1, Finance Agent v2.1…).
- Muse Wonder Full (max): 9 new results (Code Migration v1.1, Excel Modeling Benchmark v1.1, Finance Agent v2.1…).
- DeepSeek V4 Pro (max): BenchLeader Index 62.6 → 60.8.
- Muse Wonder Full (max): 1 new result (FrontierCode 1.1 (AA)).
- Muse Wonder Full (max): BenchLeader Index 68.6 → 70.7.
- DeepSeek V4 Pro (max): 1 new result (FrontierCode 1.1 (AA)); 2 score revisions.
- Muse Wonder Full (max): 1 new result (Arena WebDev); 2 score revisions.
- DeepSeek V4 Pro (max): BenchLeader Index 61.0 → 62.6.
- DeepSeek V4 Pro (max): 7 new results (USAMO 2026, IFBench, τ²-Bench Telecom (AA)…); 4 score revisions.
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- DeepSeek V4 Flash vs Muse Wonder Full
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
DeepSeek V4 Pro vs Muse Wonder Full: questions
- Is DeepSeek V4 Pro better than Muse Wonder Full?
- Muse Wonder Full (max) leads on quality: 70.7 vs 60.8. The BenchLeader Index combines every independent quality benchmark; Muse Wonder Full (max) is ahead overall as of 2026-10-24, but check the category scores for your use.
- Is DeepSeek V4 Pro better than Muse Wonder Full for coding?
- Muse Wonder Full scores higher in coding (72 vs 58 on the category index, where 50 is average). On IOI, the top shared coding benchmark, it is 51.6% for DeepSeek V4 Pro against 79.8% for Muse Wonder Full.
- Which is better for long documents?
- Muse Wonder Full scores higher on long-context benchmarks (67 vs 65). Both take about 1M tokens: DeepSeek V4 Pro 1,048,576, Muse Wonder Full 1,000,000.
- Do independent benchmarks agree on DeepSeek V4 Pro vs Muse Wonder Full?
- Across the 37 benchmarks both have, DeepSeek V4 Pro leads 1, Muse Wonder Full leads 36. Every overall index that rates both puts Muse Wonder Full ahead.
- When was Muse Wonder Full released?
- Muse Wonder Full was released on 12 Oct 2026.