Qwen3.8 Max Preview vs Laguna M.1 vs Kimi K2.7 Code Highspeed
Laguna M.1 comes out ahead, 68 to 47 and 44 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3.8 Max Preview
47/100- ECI—
- Price$2.00 / $6.00
- Context1M
- Our pick
PoolsideLaguna M.1
68/100- ECI—
- PriceFree / Free
- Context262K
Moonshot AIKimi K2.7 Code Highspeed
44/100- ECI—
- Price$1.90 / $8.00
- Context262K
Laguna M.1 is our pick
Laguna M.1 is the better all-round choice, scoring 68/100 against Qwen3.8 Max Preview (47) and Kimi K2.7 Code Highspeed (44). It leads on price. Qwen3.8 Max Preview wins on context window. Kimi K2.7 Code Highspeed wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. 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 priceLaguna M.1Laguna M.1 Free · Qwen3.8 Max Preview $3.00 · Kimi K2.7 Code Highspeed $3.42 per 1M tokens (3:1 blend)
- Longest contextQwen3.8 Max PreviewQwen3.8 Max Preview 1,000,000 · Laguna M.1 262,144 · Kimi K2.7 Code Highspeed 262,144 tokens
- Widest inputsQwen3.8 Max Preview and Kimi K2.7 Code HighspeedQwen3.8 Max Preview: Text, Images, Video · Laguna M.1: Text · Kimi K2.7 Code Highspeed: Text, Images, Video
- Self-hostingLaguna M.1 and Kimi K2.7 Code HighspeedPublishes downloadable weights
| Measure | Weight | Qwen3.8 Max Preview | Laguna M.1 | Kimi K2.7 Code Highspeed |
|---|---|---|---|---|
| Price | 50% | 27 | 100 | 25 |
| Inputs & features | 30% | 70 | 35 | 80 |
| Context window | 20% | 60 | 37 | 37 |
| Overall | 100% | 47/100 | 68/100 | 44/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3.8 Max PreviewAlibaba (Qwen) | Laguna M.1Poolside | Kimi K2.7 Code HighspeedMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $2.00 | Free (best) | $1.90 |
| Output | $6.00 | Free (best) | $8.00 |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | Free (best) | $3.42 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 6 providers | Official Poolside API | Median of 11 providers |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 262,144 tokens | 262,144 tokens |
| Max output | 131,072 tokens | 32,768 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | poolside/laguna-m.1 | — |
| API providers | 6 | 2 | 11 (best) |
| Released | Jul 19, 2026 | Apr 28, 2026 | Jun 12, 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.
- Qwen3.8 Max Preview$32.00
- Laguna M.1Free
- Kimi K2.7 Code Highspeed$35.00
Which should you choose?
Which is better: Qwen3.8 Max Preview, Laguna M.1 or Kimi K2.7 Code Highspeed?
Laguna M.1 is the better all-round choice, scoring 68/100 against Qwen3.8 Max Preview (47) and Kimi K2.7 Code Highspeed (44). It leads on price. Qwen3.8 Max Preview wins on context window. Kimi K2.7 Code Highspeed wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Qwen3.8 Max Preview, Laguna M.1 or Kimi K2.7 Code Highspeed?
Laguna M.1 is cheaper at Free input / Free output per million tokens (official Poolside API price). Qwen3.8 Max Preview costs $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). Laguna M.1 is listed as free.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3.8 Max Preview has not been scored yet, Laguna M.1 has not been scored yet and Kimi K2.7 Code Highspeed has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.8 Max Preview, Laguna M.1 and Kimi K2.7 Code Highspeed 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 Laguna M.1 and 262,144 for Kimi K2.7 Code Highspeed. Maximum output per response: Qwen3.8 Max Preview up to 131,072, Laguna M.1 up to 32,768, Kimi K2.7 Code Highspeed up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Qwen3.8 Max Preview accepts text, images and video; Laguna M.1 accepts text; Kimi K2.7 Code Highspeed accepts text, images and video. Qwen3.8 Max Preview handles the widest range of inputs.
Are any of these open source?
Laguna M.1 and Kimi K2.7 Code Highspeed publishes its weights 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. Kimi K2.7 Code Highspeed came out Jun 12, 2026; Laguna M.1 came out Apr 28, 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.
