Gemini 3.7 FlashvsMuse Spark
Gemini 3.7 Flash | Muse Spark | |
|---|---|---|
| Specifications | ||
Context window | 1M | — |
| Benchmarks | ||
Prompt injection robustness Gray Swan IPI · k = 1Attackers hide malicious instructions inside content the AI reads — a web page, an email, a document — and try to hijack what it does. Gray Swan's indirect prompt injection benchmark measures how often such an attack succeeds when the attacker gets a single try. Lower is better. | — | 2.9% |
Prompt injection robustness Gray Swan IPI · k = 10Attackers hide malicious instructions inside content the AI reads — a web page, an email, a document — and try to hijack what it does. This variant gives the attacker 10 tries and counts an attack as successful if any of them works. Lower is better. | — | 14.3% |
Prompt injection robustness Gray Swan IPI · k = 15Attackers hide malicious instructions inside content the AI reads — a web page, an email, a document — and try to hijack what it does. This variant gives the attacker 15 tries and counts an attack as successful if any of them works. Lower is better. | — | 16.5% |
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. | — | 55% |
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. | — | 77.4% |
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. | 65.3%Best | 10% |
Agentic coding FrontierCode v1.1 (Main) · main splitA set of very hard, frontier-difficulty coding tasks an AI agent has to complete end to end. The score is the share of tasks in the main split it solves. Higher is better. | 43.6% | — |
Agentic terminal coding Terminal-Bench 3.0command-line task completion (v3.0, much harder task set) | 14.9% | — |
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. | 85.8%Best | 67.3% |
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. | — | 82.2% |
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. | — | 17% |
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. | — | 49.4% |
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. | — | 50.4% |
Multidisciplinary reasoning Humanity's Last Exam (Verified)The re-checked edition of Humanity's Last Exam: the same extremely hard expert questions, minus the ones found to be flawed or wrongly answered. Scores on it run lower than on the original exam, so read the two as separate tests rather than a before-and-after. Higher is better. | 53.6% | — |
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. | — | 42.5% |
Biology BioMysteryBench · hardReal unsolved-style biology puzzles — the AI has to reason its way to an answer the way a research biologist would. The “hard” split contains the toughest cases. Higher is better. | 43.5% | — |
Biology BioMysteryBench · human solvedReal biology puzzles that human experts have managed to crack — can the AI reach the same answers? Higher is better. | 87.1% | — |
Biology LAB-Bench 2Everyday tasks from a working biology lab — reading protocols, interpreting figures and sequence data, and answering the practical questions a researcher hits at the bench. Higher is better. | 82.1% | — |
Science GPQA DiamondGraduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 89.5% |
Agentic computer use OSWorld 2.0Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.0 is a harder, refreshed task set. Higher is better. | 38.1% | — |
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. | — | 53.3% |
Agentic computer use Agent's Last ExamA hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | 26.3% | — |
Business workflows AutomationBenchTests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | 30.4% | — |
Agentic legal work Harvey's Legal Agent BenchmarkHarvey's test of whether an AI agent can complete real legal work — drafting and reviewing documents, working with spreadsheets and presentations, and navigating files the way a lawyer's assistant would. Higher is better. | 90.7% | — |
Overall intelligence AA Intelligence IndexArtificial Analysis composite intelligence index across evals | 56 | — |
Knowledge work GDPval-AA v2economically valuable knowledge work (v2, re-based Elo) | 1525 | — |
Chart reasoning CharXiv ReasoningCan the AI read and reason about complex charts and figures, not just text? Higher is better. | 84.5% | 88.9%Best |
Document comprehension GDP.PDFReal professional PDFs — filings, reports, technical documents — with questions an expert in that field would ask. Tests whether the AI reads the page as a document, layout and figures included, rather than as loose text. Higher is better. | 34% | — |
Video understanding LVBenchCan the AI follow a very long video — up to an hour — and answer questions that need details from far apart in it? Higher is better. | 85.4% | — |
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. | — | 39.9% |
Multimodal MMMUTests the AI on understanding images and text together across many college subjects. Higher is better. | — | 80.4% |
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. | 97% | — |
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. | 62.5% | — |
Community preference Arena Elo (Text)Real people chat with two anonymous AIs side by side and vote for the answer they prefer. Votes become a chess-style Elo rating on arena.ai — it measures which AI people actually like, not test scores. Higher is better. | — | 1488 |
Community preference (code) Arena Elo (Code)Like the text arena, but people vote on which AI writes better code. The votes become a chess-style Elo rating on arena.ai. Higher is better. | 1588 | — |
| Overview | ||
| Company | Meta | |
| Release date | Aug 13 2026 | Apr 8 2026 |
| Access | Proprietary | Proprietary |
Which is better: Gemini 3.7 Flash or Muse Spark?
Gemini 3.7 Flash leads Muse Spark on 2 of the 3 benchmarks they both report (DeepSWE 1.1, Terminal-Bench 2.1, CharXiv Reasoning). Muse Spark shipped 127 days before Gemini 3.7 Flash, 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 DeepSWE 1.1, Gemini 3.7 Flash leads at 65.3% vs Muse Spark at 10%. On Terminal-Bench 2.1, Gemini 3.7 Flash leads at 85.8% vs Muse Spark at 67.3%. On CharXiv Reasoning, Muse Spark leads at 88.9% vs Gemini 3.7 Flash at 84.5%.
Frequently asked questions
Gemini 3.7 Flash was released by Google on Aug 13 2026.
Muse Spark was released by Meta on Apr 8 2026.
Gemini 3.7 Flash leads on DeepSWE 1.1 — Gemini 3.7 Flash 65.3% vs Muse Spark 10%.
Other comparisons
Gemini 3.7 FlashvsClaude Opus 5Muse SparkvsClaude Opus 5Gemini 3.7 FlashvsGPT-5.6-CyberMuse SparkvsGPT-5.6-CyberGemini 3.7 FlashvsGrok 4.6Muse SparkvsGrok 4.6Gemini 3.7 FlashvsDeepSeek-V4-Pro-0813Muse SparkvsDeepSeek-V4-Pro-0813Gemini 3.7 FlashvsMistral Medium 3.5Muse SparkvsMistral Medium 3.5Gemini 3.7 FlashvsKimi K3Muse SparkvsKimi K3