DeepSeek V4 Pro 0424 vs Muse Wonder Full
Full profiles: DeepSeek V4 Pro 0424 (DeepSeek) and Muse Wonder Full (Meta): every benchmark, price and rank.
Updated · Data from 3 sources
As of 22 Oct 2026, Muse Wonder Full (max) is the better pick for most workloads: it leads on quality (70.7 vs 59.7 on the BenchLeader Index). Muse Wonder Full leads in every category we measure both in.
DeepSeek V4 Pro 0424 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 |
|---|---|---|---|
| Reasoning | Muse Wonder Full | 95 vs 62 category score; CritPt 32.9% vs 12.9% | High |
| Knowledge | Muse Wonder Full | 71 vs 57 category score; AA-Omniscience 30.1 vs -10.7 | High |
| Long documents | Muse Wonder Full | 67 vs 62 category score; AA-LCR 83.7% vs 74.7% | 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 59.7.
- DeepSeek V4 Pro 0424 (max) is stronger in agents & tools, instruction following.
- Muse Wonder Full (max) is stronger in composite, knowledge, long context, reasoning, coding.
At a glance
| Spec | DeepSeek V4 Pro 0424 | Muse Wonder Full |
|---|---|---|
| Developer | DeepSeek | Meta |
| Released | 24 Apr 2026 | 12 Oct 2026 |
| Context window (tokens) | 1,000,000 | 1,000,000 |
| Compared at | DeepSeek V4 Pro 0424 (max) | Muse Wonder Full (max) |
| Open weights | Yes | No |
| Price in / out / cached input per 1M | $0.440 / $0.870 / – | – |
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 0424 (max) | Muse Wonder Full (max) | Leader |
|---|---|---|---|
| Reasoning | 62.2 | 95.0 | Muse Wonder Full |
| Coding | – | 72.3 | not comparable |
| Agents & tools | 63.8 | – | not comparable |
| Knowledge | 56.7 | 71.4 | Muse Wonder Full |
| Instruction following | 74.5 | – | not comparable |
| Long context | 62.3 | 67.0 | Muse Wonder Full |
| Composite | 63.7 | 92.3 | Muse Wonder Full |
Where sources disagree
Across the 11 benchmarks both have, DeepSeek V4 Pro 0424 leads 0, Muse Wonder Full leads 11. Every overall index that rates both puts Muse Wonder Full ahead.
| Overall index | DeepSeek V4 Pro 0424 (max) | Muse Wonder Full (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 59.7 | 70.7 | Muse Wonder Full |
| AA Intelligence Index v4.3.2 | 30.4 | 55.8 | 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 0424 (max) | Muse Wonder Full (max) |
|---|---|---|
| Blended price per 1M tokens | $0.548 | – |
| Input / output per 1M | $0.440 / $0.870 | – / – |
| Price per task: chat reply | $0.0004 | – |
| Price per task: summarize a 30-page report | $0.0058 | – |
| Price per task: code edit | $0.0039 | – |
| Price per task: agentic coding session | $0.030 | – |
| Price per task: structured extraction | $0.0011 | – |
| Cost to run a full benchmark suite | $0.122 | – |
| Output speed | 92 tok/s | – |
| Time to first answer | 49.0 s | – |
| Time to a complete answer | 54.4 s | – |
Each model is compared at its best-ranked reasoning-effort setting. DeepSeek V4 Pro 0424 (max) scores 59.7 and takes 49 s to its first answer token; DeepSeek V4 Pro 0424 (high) scores 57.9 in 25 s, DeepSeek V4 Pro 0424 (no reasoning) scores 50.8 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
48 benchmarks, grouped by category; each name links to the board and its leaders.
| Metric | Publisher | DeepSeek V4 Pro 0424 (max) | Muse Wonder Full (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 59.7 | 70.7 |
| Agents & tools score | BenchLeader | 63.8 | – |
| Composite score | BenchLeader | 63.7 | 92.3 |
| Instruction following score | BenchLeader | 74.5 | – |
| Knowledge score | BenchLeader | 56.7 | 71.4 |
| Long context score | BenchLeader | 62.3 | 67.0 |
| Reasoning score | BenchLeader | 62.2 | 95.0 |
| Coding score | BenchLeader | – | 72.3 |
| Blended price $/M | BenchLeader | $0.548 | – |
| Output speed | BenchLeader | 92 tok/s | – |
| Time to first answer | BenchLeader | 49.0 s | – |
| Context window | BenchLeader | 1,000,000 tokens | 1,000,000 tokens |
| Reasoning | |||
| GPQA Diamond (AA) | Artificial Analysis | 88.8% | – |
| Humanity's Last Exam (AA) | Artificial Analysis | 37.5% | 53.6% |
| CritPt | Artificial Analysis | 12.9% | 32.9% |
| MysteryMechanism | Vals AI | – | 35.1% |
| Coding | |||
| Arena WebDev | Arena (LMArena) | – | 1726 |
| SciCode (AA) | Artificial Analysis | 50.8% | 62.6% |
| IOI | Vals AI | – | 79.8% |
| Code Migration | Vals AI | – | 61.0% |
| ProgramBench | Vals AI | – | 0.5% |
| Vibe Code Bench 1-100 | Vals AI | – | 34.0% |
| Vibe Code Bench v1.1 | Vals AI | – | 86.0% |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 14.7% | 57.1% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 64.0% | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | – | 44.9% |
| Agents & tools | |||
| Terminal-Bench Hard | Artificial Analysis | 46.2% | – |
| τ²-Bench Telecom (AA) | Artificial Analysis | 96.2% | – |
| GDPval-AA v2.1 | Artificial Analysis | 32.8% | 67.8% |
| τ³-Banking (AA) | Artificial Analysis | 30.1% | – |
| ITBench SRE (AA) | Artificial Analysis | 38.3% | – |
| Analyst Agent (AA) | Artificial Analysis | 18.8% | – |
| APEX-Agents (AA) | Artificial Analysis | 24.3% | – |
| CyberBench | Vals AI | – | 73.6% |
| Finance Agent v2 | Vals AI | – | 59.1% |
| Harvey's Legal Agent Benchmark | Vals AI | – | 23.3% |
| Legal Research Bench | Vals AI | – | 53.4% |
Show all 48 benchmarks
| Metric | Publisher | DeepSeek V4 Pro 0424 (max) | Muse Wonder Full (max) |
|---|---|---|---|
| Agents & tools | |||
| SREBench | Vals AI | – | 11.8% |
| Tax Agent Bench | Vals AI | – | 43.0% |
| Terminal-Bench 4.0 (Vals) | Vals AI | – | 58.1% |
| Terminal-Bench Science | Vals AI | – | 37.1% |
| AutomationBench | Artificial Analysis | 56.3% | 71.3% |
| GDP.pdf (AA) | Artificial Analysis | 13.4% | 32.6% |
| Harvey LAB-AA v1.1 | Harvey | – | 13.1% |
| EnterpriseOps-Gym | Artificial Analysis | 40.4% | – |
| AA-Briefcase v1.1 | Artificial Analysis | – | 1729 |
| Knowledge | |||
| AA-Omniscience | Artificial Analysis | -10.7 | 30.1 |
| LegalBench | Vals AI | – | 87.4% |
| BioMysteryBench | Vals AI | – | 73.3% |
| Excel Modeling Benchmark | Vals AI | – | 71.9% |
| MedCode | Vals AI | – | 49.9% |
| MedScribe | Vals AI | – | 89.3% |
| Public Benefits Bench | Vals AI | – | 73.8% |
| AA-Omniscience: accuracy | Artificial Analysis | 43.0% | 43.2% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 5.9% | 77.0% |
| Instruction following | |||
| IFBench | Artificial Analysis | 76.5% | – |
| Long context | |||
| AA-LCR | Artificial Analysis | 74.7% | 83.7% |
| MLCR | Artificial Analysis | 9.4% | – |
| Composite | |||
| AA Intelligence Index v4.3.2 | Artificial Analysis | 30.4 | 55.8 |
| Vals Index | Vals AI | – | 66.8 |
Data as of 2026-10-22. Best configuration of each model; every score links to its source on the model pages.
What changed
- Muse Wonder Full (max): 1 new result (FrontierCode 1.1 (AA)).
- Muse Wonder Full (max): BenchLeader Index 68.6 → 70.7.
- Muse Wonder Full (max): 1 new result (Arena WebDev); 2 score revisions.
- Muse Wonder Full (max): 21 new results (IOI, LegalBench, Vals Index…).
- Muse Wonder Full (max): 1 new result (Harvey LAB-AA v1.1).
- Muse Wonder Full (max): first results.
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- Muse Wonder Full vs GPT-6 Astra
- DeepSeek V4 Pro 0424 vs GPT-6 Astra
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
DeepSeek V4 Pro 0424 vs Muse Wonder Full: questions
- Is DeepSeek V4 Pro 0424 better than Muse Wonder Full?
- Muse Wonder Full (max) leads on quality: 70.7 vs 59.7. The BenchLeader Index combines every independent quality benchmark; Muse Wonder Full (max) is ahead overall as of 2026-10-22, but check the category scores for your use.
- Which is better for long documents?
- Muse Wonder Full scores higher on long-context benchmarks (67 vs 62). Both accept 1,000,000 tokens of context.
- Do independent benchmarks agree on DeepSeek V4 Pro 0424 vs Muse Wonder Full?
- Across the 11 benchmarks both have, DeepSeek V4 Pro 0424 leads 0, Muse Wonder Full leads 11. 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.