# Comparing `meta/muse-spark-1.2` vs `deepseek/deepseek-v4-flash-0731` Both are mid-2026 reasoning models aimed at coding/agentic work, with 1M-token context — but they sit at opposite ends of the price/openness spectrum. ## Quick verdict - **Muse Spark 1.2** = higher raw intelligence, multimodal, ~19× more expensive, proprietary (Meta-only hosting). - **DeepSeek V4 Flash 0731** = open weights (MIT), ~10–20× cheaper, text-only, very fast, can run locally. --- ## Identity & availability | | meta/muse-spark-1.2 | deepseek/deepseek-v4-flash-0731 | |---|---|---| | Developer | Meta (proprietary) | DeepSeek (open weights, MIT) | | Released | Aug 5, 2026 | Jul 31, 2026 (GA of V4 Flash) | | Architecture | Reasoning model, multimodal (text, image, video, audio, PDF in; text out) | 284B MoE / 13B active, DSpark speculative decoding; text-only | | Context / max out | 1M (1,048,576) | 1M (1,310,720); max output 262,144 | | Hosting | Single provider (Meta) on OpenRouter | 30 providers on OpenRouter; also runs locally (vLLM, SGLang, LM Studio) | Sources: [OpenRouter: Muse Spark 1.2](https://openrouter.ai/meta/muse-spark-1.2), [OpenRouter: DeepSeek V4 Flash 0731](https://openrouter.ai/deepseek/deepseek-v4-flash-0731), [Hugging Face model card](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731) ## Pricing (per 1M tokens) | | Muse Spark 1.2 | DeepSeek V4 Flash 0731 | |---|---|---| | OpenRouter | **$1.25 in / $4.25 out** (cached $0.15) | **$0.065 in / $0.14 out** (aggregate) | | First-party API | $1.25/$4.25; "contributor" tier $0.10/$0.20 | $0.14 in / $0.28 out; cache hit $0.0028 (~98% discount) | | AA cost per Intelligence-Index task | $0.40 | $0.11 | Sources: [OpenRouter Muse Spark 1.2](https://openrouter.ai/meta/muse-spark-1.2), [Meta developer page](https://developer.meta.com/ai/models/muse-spark/), [OpenRouter DeepSeek V4 Flash 0731](https://openrouter.ai/deepseek/deepseek-v4-flash-0731), [AA DeepSeek article](https://artificialanalysis.ai/articles/deepseek-v4-flash-0731-scores-50-on-the-artificial-analysis-intelligence-index-10-points-above-previous-deepseek-v4-flash) ## Intelligence (Artificial Analysis Intelligence Index, v4.1.1 — same harness) | | Muse Spark 1.2 (xhigh) | DeepSeek V4 Flash 0731 (max) | |---|---|---| | AA Intelligence Index at launch | **54** | **50** | | AA Intelligence Index (current model page) | **57** (#15/182 overall) | **52** (#5/107 in open-weights class) | Per-benchmark (AA harness, at-launch numbers): | Benchmark | Muse Spark 1.2 | DeepSeek V4 Flash 0731 | |---|---|---| | GDPval-AA v2 (agentic work, Elo) | **1631** (#5 overall) | 1559 | | Terminal-Bench 2.1 (agentic coding) | **80%** | 79% | | τ³-Banking (tool use) | 27% | **31%** | | SciCode | **56%** | 50% | | Humanity's Last Exam | **44%** | 37% | | CritPt | **18%** | 17% | | GPQA Diamond | n/a | 91% | | AA-LCR (long context) | n/a | 66% | | AA-Omniscience Index | **22** (halluc. rate 28%, accuracy 38%) | −16 (halluc. rate 84%, accuracy 37%) | Sources: [AA Muse Spark article](https://artificialanalysis.ai/articles/muse-spark-1-2), [AA Muse Spark model page](https://artificialanalysis.ai/models/muse-spark-1-2), [AA DeepSeek article](https://artificialanalysis.ai/articles/deepseek-v4-flash-0731-scores-50-on-the-artificial-analysis-intelligence-index-10-points-above-previous-deepseek-v4-flash), [AA DeepSeek model page](https://artificialanalysis.ai/models/deepseek-v4-flash) **Read on the Omniscience gap:** Muse Spark 1.2's low hallucination rate comes with heavy abstention (it refuses to answer when unsure — attempt rate dropped to 67%), while DeepSeek answers aggressively but hallucinates at an 84% rate (unchanged accuracy of 37%). ## DeepSeek's vendor-stated agent benchmarks (own harness — not yet independently verified) Terminal-Bench 2.1 **82.7%** (vs 61.8% preview, 72.1% V4-Pro Preview), NL2Repo 54.2%, Cybergym 76.7%, DeepSWE 54.4%, Toolathlon-Verified 70.3%, Agents' Last Exam 25.2%, AutomationBench 25.1%, DSBench-FullStack 68.7%, DSBench-Hard 59.6%. Caveat: run with DeepSeek's own "minimal mode, max effort" harness config, so not directly comparable to other labs' runs. Sources: [Hugging Face model card](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731), [AIToolsRecap analysis](https://aitoolsrecap.com/Blog/deepseek-v4-flash-0731-review-benchmarks-2026), [Digital Applied](https://www.digitalapplied.com/blog/deepseek-v4-flash-0731-official-release-agent-benchmarks) ## Speed & efficiency - **DeepSeek** is notably fast: ~120 output tokens/sec (AA, #8/107 in its class) — but **very verbose** (~210M output tokens to run the AA Intelligence Index). - **Muse Spark 1.2** has no AA speed rating yet but is far more economical with tokens (~95M for the same index). Sources: [AA DeepSeek model page](https://artificialanalysis.ai/models/deepseek-v4-flash), [AA Muse Spark model page](https://artificialanalysis.ai/models/muse-spark-1-2) ## Use cases & ecosystem - **Muse Spark 1.2**: Meta positions it for multi-agent orchestration (main agent or parallel subagent), structured output, parallel function calling, configurable reasoning effort; claims strength in multi-file refactors, long debugging sessions, whole-repository generation; powers Meta's **Muse Code** terminal agent. [OpenRouter](https://openrouter.ai/meta/muse-spark-1.2), [Meta developer page](https://developer.meta.com/ai/models/muse-spark/) - **DeepSeek V4 Flash 0731**: positioned as the cheap, fast agentic workhorse — DeepSeek shipped it as GA *before* its bigger V4-Pro flagship, claiming its agent scores "far exceed" V4-Pro Preview; supports `low/high/max` reasoning effort, thinking & non-thinking modes, Responses-API native / Codex-compatible, 2,500 concurrent requests, and runs locally thanks to MIT weights. [Digital Applied](https://www.digitalapplied.com/blog/deepseek-v4-flash-0731-official-release-agent-benchmarks), [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731) ## Bottom line - **Pick Muse Spark 1.2** if you want top-tier agentic intelligence (higher AA Index, #5 on GDPval-AA v2, much lower hallucination), need multimodal input, and budget isn't the constraint. - **Pick DeepSeek V4 Flash 0731** if you want near-frontier agentic coding at a fraction of the cost ($0.065–0.14/M input vs $1.25), need speed at scale, or want open weights you can self-host and fine-tune — accepting text-only input, heavy verbosity, and a much higher hallucination rate. - For agentic coding specifically, they're essentially neck-and-neck on AA's Terminal-Bench 2.1 (80% vs 79%), with DeepSeek claiming 82.7% on its own harness — the deciding factors are really price, modality, and openness.