GPT-5 Chat vs Kimi K2.7 Code Highspeed vs Jamba Large
Kimi K2.7 Code Highspeed comes out ahead, 44 to 34 and 30 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5 Chat
34/100- ECI—
- Price$1.25 / $10.00
- Context400K
- Our pick
Moonshot AIKimi K2.7 Code Highspeed
44/100- ECI—
- Price$1.90 / $8.00
- Context262K
AI21 LabsJamba Large
30/100- ECI—
- Price$2.00 / $8.00
- Context256K
Kimi K2.7 Code Highspeed is our pick
Kimi K2.7 Code Highspeed is the better all-round choice, scoring 44/100 against GPT-5 Chat (34) and Jamba Large (30). It leads on inputs & features. GPT-5 Chat wins on context window. 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 priceKimi K2.7 Code HighspeedKimi K2.7 Code Highspeed $3.42 · GPT-5 Chat $3.44 · Jamba Large $3.50 per 1M tokens (3:1 blend)
- Longest contextGPT-5 ChatGPT-5 Chat 400,000 · Kimi K2.7 Code Highspeed 262,144 · Jamba Large 256,000 tokens
- Widest inputsKimi K2.7 Code HighspeedGPT-5 Chat: Text, Images · Kimi K2.7 Code Highspeed: Text, Images, Video · Jamba Large: Text
- Self-hostingKimi K2.7 Code Highspeed and Jamba LargePublishes downloadable weights
| Measure | Weight | GPT-5 Chat | Kimi K2.7 Code Highspeed | Jamba Large |
|---|---|---|---|---|
| Price | 50% | 24 | 25 | 24 |
| Inputs & features | 30% | 45 | 80 | 35 |
| Context window | 20% | 44 | 37 | 36 |
| Overall | 100% | 34/100 | 44/100 | 30/100 |
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 | GPT-5 ChatOpenAI | Kimi K2.7 Code HighspeedMoonshot AI | Jamba LargeAI21 Labs |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.25 (best) | $1.90 | $2.00 |
| Output | $10.00 | $8.00 (best) | $8.00 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.44 | $3.42 (best) | $3.50 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 11 providers | Official AI21 Labs API |
| Limits | |||
| Context window | 400,000 tokens (best) | 262,144 tokens | 256,000 tokens |
| Max output | 128,000 tokens | 262,144 tokens (best) | 4,096 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 | Yes | Yes | No |
| Tool calling | No | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | — | jamba-large |
| API providers | 2 | 11 (best) | 1 |
| Released | Aug 7, 2025 | Jun 12, 2026 | Jul 1, 2025 |
| Knowledge cutoff | Sep 30, 2024 | Jan 2025 | Aug 22, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5 Chat$32.50
- Kimi K2.7 Code Highspeed$35.00
- Jamba Large$36.00
Which should you choose?
Which is better: GPT-5 Chat, Kimi K2.7 Code Highspeed or Jamba Large?
Kimi K2.7 Code Highspeed is the better all-round choice, scoring 44/100 against GPT-5 Chat (34) and Jamba Large (30). It leads on inputs & features. GPT-5 Chat wins on context window. 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, GPT-5 Chat, Kimi K2.7 Code Highspeed or Jamba Large?
Kimi K2.7 Code Highspeed is cheaper at $1.90 input / $8.00 output per million tokens (median across 11 API providers). GPT-5 Chat costs $1.25 input / $10.00 output per million tokens (median across 2 API providers); Jamba Large costs $2.00 input / $8.00 output per million tokens (official AI21 Labs API price). At a typical mix of three input tokens to one output token, that is $3.42 per million tokens for Kimi K2.7 Code Highspeed versus $3.44 for GPT-5 Chat (1× as much) and $3.50 for Jamba Large (1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5 Chat has not been scored yet, Kimi K2.7 Code Highspeed has not been scored yet and Jamba Large has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5 Chat, Kimi K2.7 Code Highspeed and Jamba Large yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that GPT-5 Chat does not support tool calling, which most coding agents need.
Which has the bigger context window?
GPT-5 Chat has the largest context window at 400,000 tokens, against 262,144 for Kimi K2.7 Code Highspeed and 256,000 for Jamba Large. Maximum output per response: GPT-5 Chat up to 128,000, Kimi K2.7 Code Highspeed up to 262,144, Jamba Large up to 4,096 tokens.
Which can read images, PDFs, audio or video?
GPT-5 Chat accepts text and images; Kimi K2.7 Code Highspeed accepts text, images and video; Jamba Large 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 Jamba Large publishes its weights and can be self-hosted; GPT-5 Chat is proprietary.
Which is newer?
Kimi K2.7 Code Highspeed is the newest, released Jun 12, 2026. GPT-5 Chat came out Aug 7, 2025; Jamba Large came out Jul 1, 2025. Knowledge cutoff: GPT-5 Chat Sep 30, 2024, Kimi K2.7 Code Highspeed Jan 2025, Jamba Large Aug 22, 2024.
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.
