Kimi K2.7 Code vs SOMA X v0.3.0
At a Glance
| Compare |  Kimi K2.7 CodeMoonshot AI |  SOMA X v0.3.0NVIDIA |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | #9 of 44$0.043 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.68Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $3.30Openrouter ↗ · Sep 23, 2026 | Not reported |
| Context windowMaximum documented tokens | 262K | Not reported |
| Model facts checked | Sep 3, 2026View model evidence → | Sep 2, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Kimi K2.7 Code | SOMA-X v0.3.0 |
|---|---|---|
| Developer | Moonshot AI | NVIDIA |
| Family | Kimi K2 7 | Soma X |
| Model | Kimi K2.7 Code | SOMA-X v0.3.0 |
| Version | Kimi K2.7 Code | SOMA-X v0.3.0 |
| Lifecycle | active | active |
| Released | 2026-06-11 | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video | Model-specific input |
| Output modalities | Text | 3D |
| Context window | 262K | Unknown |
| Total parameters | 1T | Unknown |
| Active parameters | 32B | Unknown |
| License | modified-mit | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard) | Unknown |
| Capabilities | agents, chat, coding, reasoning, tools, vision | animation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulation |
Kimi K2.7 Code Capabilities
SOMA X v0.3.0 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Kimi K2.7 Code vs SOMA X v0.3.0 FAQs
Is Kimi K2.7 Code or SOMA X v0.3.0 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K2.7 Code and SOMA X v0.3.0, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Kimi K2.7 Code or SOMA X v0.3.0?+
Only Kimi K2.7 Code has a directly sourced input price: $0.68 per million tokens. Only Kimi K2.7 Code has a directly sourced output price: $3.30 per million tokens.
Which has a larger context window, Kimi K2.7 Code or SOMA X v0.3.0?+
Neither model has a larger sourced context window in this comparison. Kimi K2.7 Code is 262K and SOMA X v0.3.0 is —.
Which performs better in benchmarks, Kimi K2.7 Code or SOMA X v0.3.0?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Kimi K2.7 Code or SOMA X v0.3.0 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Kimi K2.7 Code is open weight; SOMA X v0.3.0 is open weight.
Can Kimi K2.7 Code and SOMA X v0.3.0 understand images?+
Kimi K2.7 Code is documented with image input; SOMA X v0.3.0 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K2.7 Code or SOMA X v0.3.0?+
Neither has a larger sourced maximum output. Kimi K2.7 Code is — and SOMA X v0.3.0 is —.
Do Kimi K2.7 Code and SOMA X v0.3.0 support reasoning and tool use?+
Kimi K2.7 Code: reasoning, tool calling, and image input. SOMA X v0.3.0: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K2.7 Code or SOMA X v0.3.0?+
Kimi K2.7 Code has 4 sourced provider routes; SOMA X v0.3.0 has 0, so Kimi K2.7 Code has broader tracked availability.
Which offers better value, Kimi K2.7 Code or SOMA X v0.3.0?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.