Gemini 3.0 Flash vs Muse Spark 1.1
Google Gemini 3.0 Flash | Meta Muse Spark 1.1 | |
|---|---|---|
| Overview | ||
| Company | Meta | |
| Release date | Dec 17 2025 | Jul 9 2026 |
| Access | Proprietary | Proprietary |
| Benchmarks | ||
Agentic coding SWE-Bench ProCan the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | 49.6% | 61.5%Best |
Coding SWE-Bench VerifiedReal coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 78% | — |
Agentic coding DeepSWE 1.1Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | — | 53.3% |
Agentic terminal coding Terminal-Bench 2.1Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 58% | 80%Best |
Multi-step tool use MCP AtlasCan the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | 62% | 88.1%Best |
Professional tool use JobBenchTests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | — | 54.7% |
Personal tool use Toolathlon-VerifiedTests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | — | 75.6% |
General tool use ToolathlonTests how well the AI uses everyday real-world tools and apps to get things done. Higher is better. | 49.4% | — |
Multidisciplinary reasoning Humanity's Last Exam · no toolsHumanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 33.7% | — |
Multidisciplinary reasoning Humanity's Last Exam · with toolsHumanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 62.1% |
Abstract reasoning ARC-AGI-2Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 33.6% | — |
Science GPQA DiamondGraduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 90.4% | — |
Agentic computer use OSWorld-VerifiedCan the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 65.1% | 80.8%Best |
Agentic financial analysis Finance Agent v2Tests the AI on real financial-analysis work, like digging through reports and making sound decisions. Higher is better. | 42.6% | 57.2%Best |
Knowledge work GDPval-AAMeasures how well the AI does economically valuable knowledge work, judged against human experts. Shown as a rating (like a chess Elo) — higher is better. | 1204 | — |
Chart reasoning CharXiv ReasoningCan the AI read and reason about complex charts and figures, not just text? Higher is better. | 80.3% | 88.4%Best |
Visual reasoning BabyVisionTests core visual reasoning — seeing and understanding images the way even young children can, which AIs often find surprisingly hard. Higher is better. | — | 76.3% |
Multimodal reasoning MMMU-ProA tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 81.2% | — |
Spatial reasoning Blueprint-Bench 2Can the AI reason about space and layout — for example, understanding a floor plan or blueprint? Higher is better. | 0% | — |
Long context MRCR v2 (8-needle) · 128k averageTests whether the AI can find specific details buried inside a very long document (around 128k tokens — roughly a long book). Higher is better. | 67.2% | — |
Long context MRCR v2 (8-needle) · 1M pointwiseTests whether the AI can find specific details buried inside an enormous document (around 1 million tokens — many books). Higher is better. | 22.1% | — |
| Timeline | ||
| Release gap | Gemini 3.0 Flash shipped 204 days before Muse Spark 1.1 | |
Which is better: Gemini 3.0 Flash or Muse Spark 1.1?
Muse Spark 1.1 leads Gemini 3.0 Flash on 6 of the 6 benchmarks they both report. Gemini 3.0 Flash shipped 204 days before Muse Spark 1.1, so benchmark comparisons should account for the intervening progress.
Published specifications for these two models are limited — see each model page for the latest details.
On SWE-Bench Pro, Muse Spark 1.1 leads at 61.5% vs Gemini 3.0 Flash at 49.6%. On Terminal-Bench 2.1, Muse Spark 1.1 leads at 80% vs Gemini 3.0 Flash at 58%. On MCP Atlas, Muse Spark 1.1 leads at 88.1% vs Gemini 3.0 Flash at 62%. On OSWorld-Verified, Muse Spark 1.1 leads at 80.8% vs Gemini 3.0 Flash at 65.1%. On Finance Agent v2, Muse Spark 1.1 leads at 57.2% vs Gemini 3.0 Flash at 42.6%. On CharXiv Reasoning, Muse Spark 1.1 leads at 88.4% vs Gemini 3.0 Flash at 80.3%.
Frequently asked questions
Gemini 3.0 Flash was released by Google on Dec 17 2025.
Muse Spark 1.1 was released by Meta on Jul 9 2026.
Muse Spark 1.1 leads on SWE-Bench Pro — Gemini 3.0 Flash 49.6% vs Muse Spark 1.1 61.5%.
Other comparisons
Gemini 3.0 Flash vs Claude Sonnet 5Muse Spark 1.1 vs Claude Sonnet 5Gemini 3.0 Flash vs GPT-5.6 SolMuse Spark 1.1 vs GPT-5.6 SolGemini 3.0 Flash vs Grok 4.5Muse Spark 1.1 vs Grok 4.5Gemini 3.0 Flash vs DeepSeek-V4-ProMuse Spark 1.1 vs DeepSeek-V4-ProGemini 3.0 Flash vs Mistral Medium 3.5Muse Spark 1.1 vs Mistral Medium 3.5Gemini 3.0 Flash vs Kimi K2.7 CodeMuse Spark 1.1 vs Kimi K2.7 Code