DeepSeek-V4-Flash-0731vsMuse Spark 1.3
DeepSeek-V4-Flash-0731 | Muse Spark 1.3 | |
|---|---|---|
| Specifications | ||
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | — | 1M |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.22 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $0.66 | — |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.007 | — |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.05OpenInference | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.16OpenInference | — |
| Benchmarks | ||
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 82.7% | 88.8% |
AutomationBenchBusiness workflows — Tests 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. | 25.1% | 49.4% |
| BenchmarksPublished by one model only | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 38% | — |
SWE-Bench VerifiedCoding — Real 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. | 79% | — |
DeepSWE 1.1Agentic coding — Artificial 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. | — | 75.4% |
SWEAtlas CodeBase QnACodebase understanding — Questions about how an unfamiliar codebase actually works — where something is handled, what a change would touch — answered by reading the repository rather than editing it. Tests understanding rather than patch-writing. Higher is better. | — | 59.4% |
JobBenchProfessional tool use — Tests 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. | — | 64.9% |
Toolathlon-VerifiedPersonal tool use — Tests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | 70.3% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 73.2% | — |
DeepSearchQAAgentic browsing — Questions that cannot be answered from one page: the AI has to search the web, follow the trail across several sources, and put the pieces together into an answer. Higher is better. | — | 89.4% |
CyberGymCybersecurity — Tests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | 76.7% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity'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. | 34.8% | — |
Agentic IF Index (Internal)Instruction following — Meta's internal measure of whether a model keeps following the instructions it was given while working as an agent — over a long run of tool calls, not just in a single reply. Higher is better. | — | 57.8% |
OSWorld 2.0Agentic computer use — Can 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. | — | 66.9% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | — | 1754 |
MRCR · 256k-512kLong context — Tests whether the AI can find specific details buried inside a very long document, here across inputs of roughly 256k to 512k tokens — several books' worth of text. Higher is better. | — | 98.5% |
MRCR · 512k-1MLong context — The same buried-detail retrieval test run on even longer inputs, from roughly 512k up to a million tokens. Scores usually slip as the document grows, so read it against the shorter span above. Higher is better. | — | 98.1% |
| Overview | ||
| Company | DeepSeek | Meta |
| Release date | Jul 31 2026 | Sep 2 2026 |
| Access | Proprietary | Proprietary |
Other comparisons
DeepSeek-V4-Flash-0731vsClaude Fable 5.1Muse Spark 1.3vsClaude Fable 5.1DeepSeek-V4-Flash-0731vsGPT-5.6 SolMuse Spark 1.3vsGPT-5.6 SolDeepSeek-V4-Flash-0731vsGemini 3.8 FlashMuse Spark 1.3vsGemini 3.8 FlashDeepSeek-V4-Flash-0731vsGrok 4.6Muse Spark 1.3vsGrok 4.6DeepSeek-V4-Flash-0731vsMistral Medium 3.5Muse Spark 1.3vsMistral Medium 3.5DeepSeek-V4-Flash-0731vsKimi K3Muse Spark 1.3vsKimi K3
Frequently asked questions
Muse Spark 1.3 leads DeepSeek-V4-Flash-0731 on 2 of the 2 benchmarks they both report (Terminal-Bench 2.1, AutomationBench). Only DeepSeek-V4-Flash-0731 has a verified first-party API price: $0.22 per million input tokens and $0.66 per million output tokens. No pay-as-you-go API rate is tracked for Muse Spark 1.3. DeepSeek-V4-Flash-0731 shipped 33 days before Muse Spark 1.3, 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 Terminal-Bench 2.1, Muse Spark 1.3 leads at 88.8% vs DeepSeek-V4-Flash-0731 at 82.7%. On AutomationBench, Muse Spark 1.3 leads at 49.4% vs DeepSeek-V4-Flash-0731 at 25.1%.
DeepSeek-V4-Flash-0731 was released by DeepSeek on Jul 31 2026.
Muse Spark 1.3 was released by Meta on Sep 2 2026.
Muse Spark 1.3 leads on Terminal-Bench 2.1 — DeepSeek-V4-Flash-0731 82.7% vs Muse Spark 1.3 88.8%.
Only DeepSeek-V4-Flash-0731 has a verified first-party API price: $0.22 per million input tokens and $0.66 per million output tokens. No pay-as-you-go API rate is tracked for Muse Spark 1.3. Rates are pay-as-you-go API prices verified on August 18, 2026.