Pareto vs Kimi K2.7 Code Highspeed vs Qwen3.8 2.4T A95B
Kimi K2.7 Code Highspeed comes out ahead, 44 to 34 and 34 on our weighted score, though Qwen3.8 2.4T A95B is 12% cheaper per token.
Unbiased
Pareto
34/100- ECI—
- Price$2.50 / $7.50
- Context262K
- Our pick
Moonshot AIKimi K2.7 Code Highspeed
44/100- ECI—
- Price$1.90 / $8.00
- Context262K
Alibaba (Qwen)Qwen3.8 2.4T A95B
34/100- ECI—
- Price$2.00 / $6.00
- Context262K
Kimi K2.7 Code Highspeed is our pick
Kimi K2.7 Code Highspeed is the better all-round choice, scoring 44/100 against Qwen3.8 2.4T A95B (34) and Pareto (34). It leads on inputs & features. Qwen3.8 2.4T A95B wins on price. 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 priceQwen3.8 2.4T A95BQwen3.8 2.4T A95B $3.00 · Kimi K2.7 Code Highspeed $3.42 · Pareto $3.75 per 1M tokens (3:1 blend)
- Longest contextAbout the samePareto 262,144 · Kimi K2.7 Code Highspeed 262,144 · Qwen3.8 2.4T A95B 262,144 tokens
- Widest inputsKimi K2.7 Code HighspeedPareto: Text, Images · Kimi K2.7 Code Highspeed: Text, Images, Video · Qwen3.8 2.4T A95B: Text
- Self-hostingKimi K2.7 Code Highspeed and Qwen3.8 2.4T A95BPublishes downloadable weights (qwen3.8-max)
| Measure | Weight | Pareto | Kimi K2.7 Code Highspeed | Qwen3.8 2.4T A95B |
|---|---|---|---|---|
| Price | 50% | 23 | 25 | 27 |
| Inputs & features | 30% | 50 | 80 | 45 |
| Context window | 20% | 37 | 37 | 37 |
| Overall | 100% | 34/100 | 44/100 | 34/100 |
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 | ParetoUnbiased | Kimi K2.7 Code HighspeedMoonshot AI | Qwen3.8 2.4T A95BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $2.50 | $1.90 (best) | $2.00 |
| Output | $7.50 | $8.00 | $6.00 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.75 | $3.42 | $3.00 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 4 providers | Median of 11 providers | Median of 21 providers |
| Limits | |||
| Context window | 262,144 tokens | 262,144 tokens | 262,144 tokens |
| Max output | 131,072 tokens | 262,144 tokens (best) | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Open | Openqwen3.8-max |
| API model ID | — | — | — |
| API providers | 4 | 11 | 21 (best) |
| Released | Sep 17, 2026 | Jun 12, 2026 | Aug 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.
- Pareto$40.00
- Kimi K2.7 Code Highspeed$35.00
- Qwen3.8 2.4T A95B$32.00
Which should you choose?
Which is better: Pareto, Kimi K2.7 Code Highspeed or Qwen3.8 2.4T A95B?
Kimi K2.7 Code Highspeed is the better all-round choice, scoring 44/100 against Qwen3.8 2.4T A95B (34) and Pareto (34). It leads on inputs & features. Qwen3.8 2.4T A95B wins on price. 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, Pareto, Kimi K2.7 Code Highspeed or Qwen3.8 2.4T A95B?
Qwen3.8 2.4T A95B is cheaper at $2.00 input / $6.00 output per million tokens (median across 21 API providers). Kimi K2.7 Code Highspeed costs $1.90 input / $8.00 output per million tokens (median across 11 API providers); Pareto costs $2.50 input / $7.50 output per million tokens (median across 4 API providers). At a typical mix of three input tokens to one output token, that is $3.00 per million tokens for Qwen3.8 2.4T A95B versus $3.42 for Kimi K2.7 Code Highspeed (1.1× as much) and $3.75 for Pareto (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Pareto has not been scored yet, Kimi K2.7 Code Highspeed has not been scored yet and Qwen3.8 2.4T A95B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Pareto, Kimi K2.7 Code Highspeed and Qwen3.8 2.4T A95B 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?
Pareto, Kimi K2.7 Code Highspeed and Qwen3.8 2.4T A95B share the same 262,144-token context window. Maximum output per response: Pareto up to 131,072, Kimi K2.7 Code Highspeed up to 262,144, Qwen3.8 2.4T A95B up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Pareto accepts text and images; Kimi K2.7 Code Highspeed accepts text, images and video; Qwen3.8 2.4T A95B accepts text. Kimi K2.7 Code Highspeed handles the widest range of inputs.
Are any of these open source?
Kimi K2.7 Code Highspeed and Qwen3.8 2.4T A95B publishes its weights (qwen3.8-max) and can be self-hosted; Pareto is proprietary.
Which is newer?
Pareto is the newest, released Sep 17, 2026. Qwen3.8 2.4T A95B came out Aug 12, 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.
