Kimi K2.7 Code Highspeed vs Ornith 1.0 31B vs Qwen3.8 Max Preview
Qwen3.8 Max Preview comes out ahead, 66 to 63 and 51 on our weighted score, and it is the cheaper option too.
Moonshot AIKimi K2.7 Code Highspeed
63/100- ECI—
- Price$1.90 / $8.00
- Context262K
DeepReinforce
Ornith 1.0 31B
51/100- ECI—
- Price—
- Context262K
- Our pick
Alibaba (Qwen)Qwen3.8 Max Preview
66/100- ECI—
- Price$2.00 / $6.00
- Context1M
Qwen3.8 Max Preview is our pick
Qwen3.8 Max Preview is the better all-round choice, scoring 66/100 against Kimi K2.7 Code Highspeed (63) and Ornith 1.0 31B (51). It leads on context window. Kimi K2.7 Code Highspeed wins on inputs & features. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen3.8 Max PreviewQwen3.8 Max Preview $3.00 · Kimi K2.7 Code Highspeed $3.42 per 1M tokens (3:1 blend) · Ornith 1.0 31B unpriced
- Longest contextQwen3.8 Max PreviewQwen3.8 Max Preview 1,000,000 · Kimi K2.7 Code Highspeed 262,144 · Ornith 1.0 31B 262,144 tokens
- Widest inputsKimi K2.7 Code Highspeed and Qwen3.8 Max PreviewKimi K2.7 Code Highspeed: Text, Images, Video · Ornith 1.0 31B: Text, Images · Qwen3.8 Max Preview: Text, Images, Video
- Self-hostingKimi K2.7 Code Highspeed and Ornith 1.0 31BPublishes downloadable weights (MIT)
| Measure | Weight | Kimi K2.7 Code Highspeed | Ornith 1.0 31B | Qwen3.8 Max Preview |
|---|---|---|---|---|
| Inputs & features | 60% | 80 | 60 | 70 |
| Context window | 40% | 37 | 37 | 60 |
| Overall | 100% | 63/100 | 51/100 | 66/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Kimi K2.7 Code HighspeedMoonshot AI | Ornith 1.0 31BDeepReinforce | Qwen3.8 Max PreviewAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.90 (best) | — | $2.00 |
| Output | $8.00 | — | $6.00 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.42 | — | $3.00 (best) |
| Long-context rate | Same rate | — | Same rate |
| Price source | Median of 11 providers | — | Median of 6 providers |
| Limits | |||
| Context window | 262,144 tokens | 262,144 tokens | 1,000,000 tokens (best) |
| Max output | 262,144 tokens (best) | — | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | OpenMIT | Proprietary |
| API model ID | — | — | — |
| API providers | 11 (best) | — | 6 |
| Released | Jun 12, 2026 | Jun 25, 2026 | Jul 19, 2026 |
| Knowledge cutoff | Jan 2025 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Kimi K2.7 Code Highspeed$35.00
- Ornith 1.0 31B—
- Qwen3.8 Max Preview$32.00
Which should you choose?
Which is better: Kimi K2.7 Code Highspeed, Ornith 1.0 31B or Qwen3.8 Max Preview?
Qwen3.8 Max Preview is the better all-round choice, scoring 66/100 against Kimi K2.7 Code Highspeed (63) and Ornith 1.0 31B (51). It leads on context window. Kimi K2.7 Code Highspeed wins on inputs & features. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Kimi K2.7 Code Highspeed, Ornith 1.0 31B or Qwen3.8 Max Preview?
Qwen3.8 Max Preview is cheaper at $2.00 input / $6.00 output per million tokens (median across 6 API providers). Kimi K2.7 Code Highspeed costs $1.90 input / $8.00 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $3.00 per million tokens for Qwen3.8 Max Preview versus $3.42 for Kimi K2.7 Code Highspeed (1.1× as much). Ornith 1.0 31B has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Kimi K2.7 Code Highspeed has not been scored yet, Ornith 1.0 31B has not been scored yet and Qwen3.8 Max Preview has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2.7 Code Highspeed, Ornith 1.0 31B and Qwen3.8 Max Preview yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3.8 Max Preview has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2.7 Code Highspeed and 262,144 for Ornith 1.0 31B. Maximum output per response: Kimi K2.7 Code Highspeed up to 262,144, Qwen3.8 Max Preview up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Kimi K2.7 Code Highspeed accepts text, images and video; Ornith 1.0 31B accepts text and images; Qwen3.8 Max Preview accepts text, images and video. Kimi K2.7 Code Highspeed handles the widest range of inputs.
Are any of these open source?
Kimi K2.7 Code Highspeed and Ornith 1.0 31B publishes its weights (MIT) and can be self-hosted; Qwen3.8 Max Preview is proprietary.
Which is newer?
Qwen3.8 Max Preview is the newest, released Jul 19, 2026. Ornith 1.0 31B came out Jun 25, 2026; Kimi K2.7 Code Highspeed came out Jun 12, 2026. Knowledge cutoff: Kimi K2.7 Code Highspeed Jan 2025.
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 Oct 4, 2026.
