Kimi K2.7 Code vs Qwen3.8 Max Preview vs Grok 4.3
Grok 4.3 comes out ahead, 56 to 51 and 47 on our weighted score, and it is the cheaper option too.
Moonshot AIKimi K2.7 Code
51/100- ECI150.0
- Price$0.95 / $4.00
- Context262K
Alibaba (Qwen)Qwen3.8 Max Preview
47/100- ECI—
- Price$2.00 / $6.00
- Context1M
- Our pick
xAIGrok 4.3
56/100- ECI149.2
- Price$1.25 / $2.50
- Context1M
Grok 4.3 is our pick
Grok 4.3 is the better all-round choice, scoring 56/100 against Kimi K2.7 Code (51) and Qwen3.8 Max Preview (47). It leads 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 priceGrok 4.3Grok 4.3 $1.56 · Kimi K2.7 Code $1.71 · Qwen3.8 Max Preview $3.00 per 1M tokens (3:1 blend)
- Longest contextQwen3.8 Max Preview and Grok 4.3Qwen3.8 Max Preview 1,000,000 · Grok 4.3 1,000,000 · Kimi K2.7 Code 262,144 tokens
- Widest inputsSame inputsKimi K2.7 Code: Text, Images, Video · Qwen3.8 Max Preview: Text, Images, Video · Grok 4.3: Text, Images, PDFs
- Self-hostingKimi K2.7 CodePublishes downloadable weights
| Measure | Weight | Kimi K2.7 Code | Qwen3.8 Max Preview | Grok 4.3 |
|---|---|---|---|---|
| Price | 50% | 39 | 27 | 41 |
| Inputs & features | 30% | 80 | 70 | 80 |
| Context window | 20% | 37 | 60 | 60 |
| Overall | 100% | 51/100 | 47/100 | 56/100 |
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 | Kimi K2.7 CodeMoonshot AI | Qwen3.8 Max PreviewAlibaba (Qwen) | Grok 4.3xAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 150.0 (best) | — | 149.2 |
| ECI rank | #49 of 148 (best) | — | #55 of 148 |
| GPQA DiamondGraduate-level science questions | 87.9% | — | 88.8% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 54.0% (best) | — | 42.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 95.6% (best) | — | 93.3% |
| SimpleQA VerifiedShort factual questions | 36.5% (best) | — | 33.2% |
| Price per million tokens | |||
| Input | $0.95 (best) | $2.00 | $1.25 |
| Output | $4.00 | $6.00 | $2.50 (best) |
| Cached input | $0.19 (best) | — | $0.20 |
| Blended (3:1) | $1.71 | $3.00 | $1.56 (best) |
| Long-context rate | Same rate | Same rate | Over 200K: $2.50 / $5.00 |
| Price source | Official Moonshot AI API | Median of 6 providers | Official xAI API |
| Limits | |||
| Context window | 262,144 tokens | 1,000,000 tokens (best) | 1,000,000 tokens (best) |
| Max output | 262,144 tokens (best) | 131,072 tokens | 30,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | Yes | Yes | No |
| Reasoning | Yes | Yes | Yeslow · medium · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | kimi-k2.7-code | — | grok-4.3 |
| API providers | 51 (best) | 6 | 27 |
| Released | Jun 12, 2026 | Jul 19, 2026 | Apr 17, 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$17.50
- Qwen3.8 Max Preview$32.00
- Grok 4.3$17.50
Which should you choose?
Which is better: Kimi K2.7 Code, Qwen3.8 Max Preview or Grok 4.3?
Grok 4.3 is the better all-round choice, scoring 56/100 against Kimi K2.7 Code (51) and Qwen3.8 Max Preview (47). It leads 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, Kimi K2.7 Code, Qwen3.8 Max Preview or Grok 4.3?
Grok 4.3 is cheaper at $1.25 input / $2.50 output per million tokens (official xAI API price). Kimi K2.7 Code costs $0.95 input / $4.00 output per million tokens (official Moonshot AI API price); Qwen3.8 Max Preview costs $2.00 input / $6.00 output per million tokens (median across 6 API providers). At a typical mix of three input tokens to one output token, that is $1.56 per million tokens for Grok 4.3 versus $1.71 for Kimi K2.7 Code (1.1× as much) and $3.00 for Qwen3.8 Max Preview (1.9× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Kimi K2.7 Code has an ECI of 150.0, Qwen3.8 Max Preview has not been scored yet and Grok 4.3 has an ECI of 149.2.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2.7 Code, Qwen3.8 Max Preview and Grok 4.3 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 and Grok 4.3 have the largest context windows (1,000,000 and 1,000,000 tokens), against 262,144 for Kimi K2.7 Code. Maximum output per response: Kimi K2.7 Code up to 262,144, Qwen3.8 Max Preview up to 131,072, Grok 4.3 up to 30,000 tokens.
Which can read images, PDFs, audio or video?
Kimi K2.7 Code accepts text, images and video; Qwen3.8 Max Preview accepts text, images and video; Grok 4.3 accepts text, images and PDFs. They handle the same number of input types.
Are any of these open source?
Kimi K2.7 Code publishes its weights and can be self-hosted; Qwen3.8 Max Preview and Grok 4.3 is proprietary.
Which is newer?
Qwen3.8 Max Preview is the newest, released Jul 19, 2026. Kimi K2.7 Code came out Jun 12, 2026; Grok 4.3 came out Apr 17, 2026. Knowledge cutoff: Kimi K2.7 Code 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.
