Gemini 4 Carbon vs Muse Spark Open 816B A42B
Full profiles: Gemini 4 Carbon (Google) and Muse Spark Open 816B A42B (Meta): every benchmark, price and rank.
Updated · Data from 4 sources
As of 29 Oct 2026, Gemini 4 Carbon is ahead of Muse Spark Open 816B A42B on quality (65.1 vs 58.5 on the BenchLeader Index), but Muse Spark Open 816B A42B is 4.0× cheaper at $2.00 vs $8.00 per 1M tokens blended. Gemini 4 Carbon leads in every category we measure both in.
Key facts (as of 29 Oct 2026)
- Gemini 4 Carbon scores 65.1 on the BenchLeader Index and Muse Spark Open 816B A42B scores 58.5 (BenchLeader, 29 Oct 2026).
- Gemini 4 Carbon costs $8.00 per 1M tokens blended (list price, 29 Oct 2026).
- Muse Spark Open 816B A42B costs $2.00 per 1M tokens blended (list price, 29 Oct 2026).
- Of the 1 benchmarks both models have, Gemini 4 Carbon leads 1 (BenchLeader, 29 Oct 2026).
Gemini 4 Carbon benchmarks high Google
Muse Spark Open 816B A42B benchmarks xhigh 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 | Gemini 4 Carbon | 72 vs 66 category score; SciCode 64.2% vs 58.5% | Medium |
| Reasoning | Gemini 4 Carbon | 79 vs 64 category score | High |
| Knowledge | Gemini 4 Carbon | 76 vs 62 category score | High |
| Lowest cost | Muse Spark Open 816B A42B | $2.00 vs $8.00 per 1M blended; agentic coding session $0.092 vs $0.320 | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- Gemini 4 Carbon (high) leads on quality: 65.1 vs 58.5.
- Gemini 4 Carbon (high) is stronger in coding, knowledge, long context, reasoning.
- Muse Spark Open 816B A42B (xhigh) is stronger in maths.
- Muse Spark Open 816B A42B (xhigh) is 4.0× cheaper ($2.00 vs $8.00 per 1M blended).
At a glance
| Spec | Gemini 4 Carbon | Muse Spark Open 816B A42B |
|---|---|---|
| Developer | Meta | |
| Released | 28 Oct 2026 | 15 Oct 2026 |
| Context window (tokens) | 1,000,000 | 1,048,576 |
| Compared at | Gemini 4 Carbon (high) | Muse Spark Open 816B A42B (xhigh) |
| Open weights | No | Yes |
| Price in / out / cached input per 1M | $4.00 / $20.00 / – | $1.25 / $4.25 / $0.150 |
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 | Gemini 4 Carbon (high) | Muse Spark Open 816B A42B (xhigh) | Leader |
|---|---|---|---|
| Reasoning | 78.9 | 63.6 | Gemini 4 Carbon |
| Coding | 71.7 | 65.5 | Gemini 4 Carbon |
| Maths | – | 59.4 | not comparable |
| Knowledge | 75.8 | 62.2 | Gemini 4 Carbon |
| Long context | 64.5 | – | not comparable |
Where sources disagree
Across the 1 benchmarks both have, Gemini 4 Carbon leads 1, Muse Spark Open 816B A42B leads 0. Every overall index that rates both puts Gemini 4 Carbon ahead.
| Overall index | Gemini 4 Carbon (high) | Muse Spark Open 816B A42B (xhigh) | Leader |
|---|---|---|---|
| BenchLeader Index | 65.1 | 58.5 | Gemini 4 Carbon |
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 | Gemini 4 Carbon (high) | Muse Spark Open 816B A42B (xhigh) |
|---|---|---|
| Blended price per 1M tokens | $8.00 | $2.00 |
| Input / output per 1M | $4.00 / $20.00 | $1.25 / $4.25 |
| Cached input per 1M | – | $0.150 |
| Price per task: chat reply | $0.0076 | $0.0018 |
| Price per task: summarize a 30-page report | $0.060 | $0.018 |
| Price per task: code edit | $0.054 | $0.014 |
| Price per task: agentic coding session | $0.320 | $0.092 |
| Price per task: structured extraction | $0.012 | $0.0034 |
| Cost to run a full benchmark suite | $7.76 | – |
| Output speed | 100 tok/s | – |
| Time to first answer | 103.1 s | – |
| Time to a complete answer | 108.1 s | – |
Each model is compared at its best-ranked reasoning-effort setting. Gemini 4 Carbon (high) scores 65.1 and takes 103 s to its first answer token. Muse Spark Open 816B A42B (xhigh) scores 58.5. 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
37 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 | Gemini 4 Carbon (high) | Muse Spark Open 816B A42B (xhigh) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 65.1 | 58.5 |
| Coding score | BenchLeader | 71.7 | 65.5 |
| Knowledge score | BenchLeader | 75.8 | 62.2 |
| Long context score | BenchLeader | 64.5 | – |
| Reasoning score | BenchLeader | 78.9 | 63.6 |
| Maths score | BenchLeader | – | 59.4 |
| Blended price $/M | BenchLeader | $8.00 | $2.00 |
| Output speed | BenchLeader | 100 tok/s | – |
| Time to first answer | BenchLeader | 103.1 s | – |
| Context window | BenchLeader | 1,000,000 tokens | 1,048,576 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | – | 88.6% |
| Humanity's Last Exam (AA) | Artificial Analysis | 60.4% | – |
| HLE-Diamond (AA) | Artificial Analysis | 69.1% | – |
| CritPt | Artificial Analysis | 36.0% | – |
| CritPt v2 hard subset (AA) | Artificial Analysis | 63.0% | – |
| Coding | |||
| SciCode | SciCode | 64.2% | 58.5% |
| SciCode (AA) | Artificial Analysis | 64.2% | – |
| Vibe Code Bench v1.1 | Vals AI | 89.1% | – |
| ALE-Bench | Epoch AI Benchmarking Hub | – | 1590.1 |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 65.2% | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | 48.5% | – |
| FrontierCode 2.0 (AA) | Artificial Analysis | 40.6% | – |
| Agents & tools | |||
| APEX-Agents | Mercor | – | 33.4%† |
| Finance Agent v2.1 | Vals AI | 67.2% | – |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 17.5% | – |
| Legal Research Bench | Vals AI | 55.8% | – |
| Tax Agent Bench | Vals AI | 48.9% | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | 63.1% | – |
| Vending-Bench 2 | Andon Labs | 16484.2† | – |
| AutomationBench | Artificial Analysis | 77.8% | – |
| GDP.pdf (AA) | Artificial Analysis | 28.4% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1609 | – |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 64.8% | – |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | – | 69.8% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | – | 31.7% |
Show all 37 benchmarks
| Metric | Publisher | Gemini 4 Carbon (high) | Muse Spark Open 816B A42B (xhigh) |
|---|---|---|---|
| Maths | |||
| OTIS Mock AIME | Epoch AI Benchmarking Hub | – | 98.1% |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | – | 56.6% |
| AA-Omniscience v1.1 | Artificial Analysis | 53.5 | – |
| Excel Modeling Benchmark v1.1 | Vals AI | 76.5% | – |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | 58.3% | – |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | 88.6% | – |
| Multimodal | |||
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | 58.6%† | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 79.3% | – |
| Composite | |||
| AA Intelligence Index v5.0 | Artificial Analysis | 59.4 | – |
| AA Progress Index v1 | Artificial Analysis | 351.6 | – |
| Vals Index | Vals AI | 70.4 | – |
| Vals Finance Index | Vals AI | 66.3% | – |
Data as of 2026-10-29. Best configuration of each model; every score links to its source on the model pages.
What changed
- Muse Spark Open 816B A42B (xhigh): BenchLeader Index 57.4 → 58.6.
- Muse Spark Open 816B A42B (xhigh): 2 new results (SciCode, ALE-Bench).
- Muse Spark Open 816B A42B (xhigh): first results.
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- Muse Spark Open 816B A42B vs GPT-6.1 Sol
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- Gemini 4 Argon vs Muse Spark Open 816B A42B
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
Gemini 4 Carbon vs Muse Spark Open 816B A42B: questions
- Is Gemini 4 Carbon better than Muse Spark Open 816B A42B?
- Gemini 4 Carbon (high) leads on quality: 65.1 vs 58.5. The BenchLeader Index combines every independent quality benchmark; Gemini 4 Carbon (high) is ahead overall as of 29 Oct 2026, but check the category scores for your use.
- Gemini 4 Carbon vs Muse Spark Open 816B A42B: which is cheaper, and will that change?
- Muse Spark Open 816B A42B is cheaper today: $2.00 against $8.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Gemini 4 Carbon or Muse Spark Open 816B A42B better for coding?
- Gemini 4 Carbon scores higher in coding (72 vs 66 on the category index, where 50 is average). On SciCode, the top shared coding benchmark, it is 64.2% for Gemini 4 Carbon against 58.5% for Muse Spark Open 816B A42B.
- Which is better for long documents?
- Both take about 1M tokens: Gemini 4 Carbon 1,000,000, Muse Spark Open 816B A42B 1,048,576.
- Do independent benchmarks agree on Gemini 4 Carbon vs Muse Spark Open 816B A42B?
- Across the 1 benchmarks both have, Gemini 4 Carbon leads 1, Muse Spark Open 816B A42B leads 0. Every overall index that rates both puts Gemini 4 Carbon ahead.
More questions (1)
- When was Gemini 4 Carbon released?
- Gemini 4 Carbon was released on 28 Oct 2026.