Model comparison
Kimi K3 vs Muse Spark 1.2
Moonshot AI against Meta, compared on context, price, and verified benchmark results.
Catalog record checked August 13, 2026; individual provider fields may change.
Moonshot AI
Kimi K3
Frontier · Open weights
Meta
Muse Spark 1.2
Frontier
| Specification | Kimi K3 | Muse Spark 1.2 |
|---|---|---|
| Provider | Moonshot AI | Meta |
| Tier | Frontier | Frontier |
| Context window | 1.05M | 1.05M |
| Max output | 1M | Not verified |
| Input / 1M tokens | $3 | Winner: $1.25 |
| Output / 1M tokens | $15 | Winner: $4.25 |
| Weights | Open | Closed |
| Parameters | 2.8T total / 104B active (MoE) | Not disclosed |
| Reasoning levels | low, high, max | minimal, low, medium, high, xhigh |
| Modalities | text, image, video | text, image, video, pdf |
| Released | July 16, 2026 | August 5, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-08) | Winner: 60 | 57 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Kimi K3
Open-weight frontier model scoring within a few points of the closed leaders on agentic coding.
Best for
- Agentic coding without vendor lock-in
- Self-hosting at frontier quality
- Long-context work
Watch out
2.8T parameters means self-hosting is a datacentre exercise, not a workstation one — open weights here mean provider choice, not local inference.
Muse Spark 1.2
Meta Model API's current Muse Spark checkpoint (`muse-spark-1.2`). Meta documents it as the coding-focused Spark update used with Muse Code; maximum output is not published as a hard token cap on the current model page, so it stays unverified.
Best for
- Terminal coding agents
- Long-horizon refactors
- Meta Model API stacks
Watch out
A cheaper contributor tier (`muse-spark-1.2-contributor`) permits training use of prompts and completions; standard-tier `muse-spark-1.2` does not. `reasoning_effort: none` is unsupported (HTTP 400). Weights are not marked open until an HF/license card is verified. For local agentic now, prefer Muse Glimmer 30B.
Benchmark
DeepSWE 1.1 in context
Both models shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
Kimi K3 vs Muse Spark 1.2
Answered from the verified figures on this page rather than general guidance.
Is Kimi K3 or Muse Spark 1.2 cheaper for input?
Muse Spark 1.2 is cheaper at $1.25 per million input tokens, against $3 for Kimi K3 — roughly 2.4× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate.
Is Kimi K3 or Muse Spark 1.2 cheaper for output?
Muse Spark 1.2 is cheaper at $4.25 per million output tokens, against $15 for Kimi K3 — roughly 3.5× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate.
Which has the larger context window, Kimi K3 or Muse Spark 1.2?
Both accept about 1.05M tokens of context, so document length will not decide between them.
Do Kimi K3 and Muse Spark 1.2 support the same reasoning levels?
Kimi K3 exposes low, high, max, while Muse Spark 1.2 exposes minimal, low, medium, high, xhigh.
Should I use Kimi K3 or Muse Spark 1.2?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. Kimi K3 suits agentic coding without vendor lock-in; Muse Spark 1.2 suits terminal coding agents.
Can I self-host Kimi K3 or Muse Spark 1.2?
Kimi K3 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Muse Spark 1.2 is a closed model whose supported access paths are controlled by its provider.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.