Muse Wonder vs Kimi K3 vs GPT-5.6 Sol
Too close to call on our weighted score (Muse Wonder 68, GPT-5.6 Sol 66, Kimi K3 65). The right pick depends on what you value most.
MetaMuse Wonder
68/100- ECI161.7
- Price$4.00 / $20.00
- Context1.05M
Moonshot AIKimi K3
65/100- ECI158.3
- Price$3.00 / $15.00
- Context1.05M
OpenAIGPT-5.6 Sol
66/100- ECI162.6
- Price$4.00 / $20.00
- Context1.05M
Too close to call
It is close. Our weighted score puts them within 2 points (Muse Wonder 68/100, GPT-5.6 Sol 66/100, Kimi K3 65/100), so choose by what matters most for your work: GPT-5.6 Sol for raw capability and Kimi K3 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-5.6 SolCapabilities Index (ECI): GPT-5.6 Sol 162.6 · Muse Wonder 161.7 · Kimi K3 158.3
- Lowest priceKimi K3Kimi K3 $6.00 · Muse Wonder $8.00 · GPT-5.6 Sol $8.00 per 1M tokens (3:1 blend)
- Longest contextAbout the sameGPT-5.6 Sol 1,050,000 · Muse Wonder 1,048,576 · Kimi K3 1,048,576 tokens
- Widest inputsMuse WonderMuse Wonder: Text, Images, PDFs, Audio, Video · Kimi K3: Text, Images, Video · GPT-5.6 Sol: Text, Images, PDFs
- Self-hostingKimi K3Publishes downloadable weights
| Measure | Weight | Muse Wonder | Kimi K3 | GPT-5.6 Sol |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 91 | 87 | 92 |
| Price | 25% | 7 | 12 | 7 |
| Inputs & features | 15% | 100 | 80 | 80 |
| Context window | 10% | 61 | 61 | 61 |
| Overall | 100% | 68/100 | 65/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Muse WonderMeta | Kimi K3Moonshot AI | GPT-5.6 SolOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 161.7 | 158.3 | 162.6 (best) |
| ECI rank | #13 of 165 | #20 of 165 | #12 of 165 (best) |
| GPQA DiamondGraduate-level science questions | — | 93.1% | 93.5% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 82.1% | 72.2% | 89.1% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 99.4% | 97.2% | 100% (best) |
| SimpleQA VerifiedShort factual questions | — | 50.6% | 69.7% (best) |
| Price per million tokens | |||
| Input | $4.00 | $3.00 (best) | $4.00 |
| Output | $20.00 | $15.00 (best) | $20.00 |
| Cached input | $0.20 (best) | $0.30 | $0.40 |
| Blended (3:1) | $8.00 | $6.00 (best) | $8.00 |
| Long-context rate | Same rate | Same rate | Over 272K: $8.00 / $30.00 |
| Price source | Official Meta API | Official Moonshot AI API | Official OpenAI API |
| Limits | |||
| Context window | 1,048,576 tokens | 1,048,576 tokens | 1,050,000 tokens (best) |
| Max output | 131,072 tokens (best) | 131,072 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | No | Yes |
| Audio | Yes | No | No |
| Video | Yes | Yes | No |
| Reasoning | Yesminimal · low · medium · high · xhigh · max | Yeslow · high · max | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | muse-wonder-1.0 | kimi-k3 | gpt-5.6-sol |
| API providers | 9 | 76 (best) | 49 |
| Released | Oct 12, 2026 | Jul 16, 2026 | Jul 9, 2026 |
| Knowledge cutoff | — | — | Feb 16, 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Muse Wonder$80.00
- Kimi K3$60.00
- GPT-5.6 Sol$80.00
Which should you choose?
Which is better: Muse Wonder, Kimi K3 or GPT-5.6 Sol?
It is close. Our weighted score puts them within 2 points (Muse Wonder 68/100, GPT-5.6 Sol 66/100, Kimi K3 65/100), so choose by what matters most for your work: GPT-5.6 Sol for raw capability and Kimi K3 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Muse Wonder, Kimi K3 or GPT-5.6 Sol?
Kimi K3 is cheaper at $3.00 input / $15.00 output per million tokens (official Moonshot AI API price). Muse Wonder costs $4.00 input / $20.00 output per million tokens (official Meta API price); GPT-5.6 Sol costs $4.00 input / $20.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $6.00 per million tokens for Kimi K3 versus $8.00 for Muse Wonder (1.3× as much) and $8.00 for GPT-5.6 Sol (1.3× as much).
Which scores higher on benchmarks?
GPT-5.6 Sol scores higher on the Capabilities Index (ECI): GPT-5.6 Sol 162.6 (#12 of 165), Muse Wonder 161.7 (#13 of 165) and Kimi K3 158.3 (#20 of 165). The confidence ranges of the top two overlap (159.6–166.2 vs 159.2–164.6), so treat the gap as small. On individual benchmarks: FrontierMath Tiers 1–3 — GPT-5.6 Sol 89.1%, Muse Wonder 82.1%, Kimi K3 72.2%; OTIS Mock AIME 2024–2025 — GPT-5.6 Sol 100%, Muse Wonder 99.4%, Kimi K3 97.2%.
Which is better for coding?
There are no published SWE-bench Verified results for Muse Wonder, Kimi K3 and GPT-5.6 Sol yet, so there is no like-for-like coding score. On overall capability, GPT-5.6 Sol leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Their context windows are effectively the same size: Muse Wonder 1,048,576, Kimi K3 1,048,576 and GPT-5.6 Sol 1,050,000 tokens. Maximum output per response: Muse Wonder up to 131,072, Kimi K3 up to 131,072, GPT-5.6 Sol up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Muse Wonder accepts text, images, PDFs, audio and video; Kimi K3 accepts text, images and video; GPT-5.6 Sol accepts text, images and PDFs. Muse Wonder handles the widest range of inputs.
Are any of these open source?
Kimi K3 publishes its weights and can be self-hosted; Muse Wonder and GPT-5.6 Sol is proprietary.
Which is newer?
Muse Wonder is the newest, released Oct 12, 2026. Kimi K3 came out Jul 16, 2026; GPT-5.6 Sol came out Jul 9, 2026. Knowledge cutoff: GPT-5.6 Sol Feb 16, 2026.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
