Muse Spark 1.1 vs MiMo-V2.5: which should you choose in 2026?
Muse Spark 1.1 (by Meta) and MiMo-V2.5 (by Xiaomi) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
MiMo-V2.5 is about 11.4× cheaper on a blended 3:1 input/output basis ($0.18 vs $2.00 per 1M tokens).
Choose Muse Spark 1.1 if…
- • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage
Choose MiMo-V2.5 if…
- • lower blended API cost matters for your workload
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | Muse Spark 1.1 | MiMo-V2.5 |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | 42.8% Artificial Analysis | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
Finance Agent v2 A Vals AI benchmark with 927 expert-reviewed questions modeling the work of entry-level financial analysts. Task score | 57.2% Vals AI | — |
JobBench A professional tool-use benchmark with 65 tasks spanning 35 white-collar occupations. Task score | 54.7% JobBench | — |
MCP Atlas A scaled tool-use benchmark with 1,000 multi-step tasks across 36 real MCP servers and 220 tools. Tasks completed | 88.1%±1.9 Scale AI | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | 80.8% Meta report | — |
A long-horizon software-engineering benchmark with 113 original tasks graded by hand-written tests. Pass@1 | 53.0%±3.0 DataCurve | — |
A contamination-resistant software-engineering benchmark with long-horizon tasks across multiple programming languages. Public and private splits are distinct protocols. Resolved | 61.5%±3.1 Scale AI | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | — |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | 80.0% Meta report | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | — | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | 62.1% Meta report | — |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages | 75.3% LiveBench | — |
BabyVision A 388-question visual-understanding benchmark covering fine-grained discrimination, spatial perception, tracking, and pattern recognition. Accuracy | 76.3% Meta report | — |
The chart-reasoning portion of CharXiv, evaluating visual and mathematical reasoning over scientific figures. Accuracy | 88.4% Meta report | — |
| Arena preference scores | ||
Human preference score | 1487 ±6Preliminary | 1434 ±4 |
Human preference score for code and web development | 1536 ±10Preliminary | 1437 ±7 |
Benchmark sources
- Arena AI leaderboard
- AutomationBench-AA: Agentic SaaS Workflow Benchmark
- Finance Agent v2 Leaderboard
- JobBench Leaderboard
- MCP Atlas Leaderboard
- Muse Spark 1.1 Evaluation Report
- DeepSWE 1.1 Leaderboard
- SWE-Bench Pro Public Leaderboard
- LiveBench 2026-06-25 Leaderboard
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 9, 2026.
Pricing, capabilities, and model facts
| Feature | Muse Spark 1.1 | MiMo-V2.5 |
|---|---|---|
| Context & model facts | ||
| Developer | Meta | Xiaomi |
| API provider | Meta | Xiaomi |
| Input context | 1,048,576 tokens | 1,050,000 tokens |
| Maximum output | 131,072 tokens | 131,072 tokens |
| Released | Jul 9, 2026 | — |
| Added to Writingmate | Jul 16, 2026 | Apr 22, 2026 |
| License | Proprietary | MIT |
| Knowledge cutoff | Not disclosed | — |
| Capabilities | ||
| Inputs | Text, Image, Video, File, Audio | Text, Audio, Image, Video |
| Outputs | Text | Text |
| Tool use | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | Yes | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $1.25 | $0.14 |
| Output (per 1M tokens) | $4.25 | $0.28 |
| Blended 3:1 input/output | $2.00 | $0.18 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Muse Spark 1.1 Evaluation Report
- Meta Model API provider documentation
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Jul 16, 2026.
Why Pay for Multiple Subscriptions?
Comparing Muse Spark 1.1 from Meta with MiMo-V2.5 from Xiaomi? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Muse Spark 1.1 | MiMo-V2.5 | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Muse Spark 1.1 vs MiMo-V2.5 FAQ
Which is better, Muse Spark 1.1 or MiMo-V2.5?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. MiMo-V2.5 is about 11.4× cheaper on a blended 3:1 input/output basis ($0.18 vs $2.00 per 1M tokens).
Which model is cheaper to use through an API?
MiMo-V2.5 is about 11.4× cheaper on a blended 3:1 input/output basis ($0.18 vs $2.00 per 1M tokens).
Which model supports more context?
The published context windows are close enough that this page does not treat the difference as a material advantage.
Can I switch between Muse Spark 1.1 and MiMo-V2.5?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.