GPT-5 Chat vs Jamba Large vs Kimi K2.7 Code Highspeed
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
AI21 LabsJamba Large
30/100- ECI—
- Price$2.00 / $8.00
- Context256K
- Our pick
Moonshot AIKimi K2.7 Code Highspeed
44/100- ECI—
- Price$1.90 / $8.00
- Context262K
Kimi K2.7 Code Highspeed is our pick
Kimi K2.7 Code Highspeed is the better all-round choice, scoring 44/100 against 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 · Jamba Large: Text · Kimi K2.7 Code Highspeed: Text, Images, Video
- Self-hostingJamba Large and Kimi K2.7 Code HighspeedPublishes downloadable weights
| Measure | Weight | GPT-5 Chat | Jamba Large | Kimi K2.7 Code Highspeed |
|---|---|---|---|---|
| Price | 50% | 24 | 24 | 25 |
| Inputs & features | 30% | 45 | 35 | 80 |
| Context window | 20% | 44 | 36 | 37 |
| Overall | 100% | 34/100 | 30/100 | 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 | GPT-5 ChatOpenAI | Jamba LargeAI21 Labs | Kimi K2.7 Code HighspeedMoonshot AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.25 (best) | $2.00 | $1.90 |
| Output | $10.00 | $8.00 (best) | $8.00 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.44 | $3.50 | $3.42 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official AI21 Labs API | Median of 11 providers |
| Limits | |||
| Context window | 400,000 tokens (best) | 256,000 tokens | 262,144 tokens |
| Max output | 128,000 tokens | 4,096 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 | No | No | Yes |
| Reasoning | Yes | No | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | jamba-large | — |
| API providers | 2 | 1 | 11 (best) |
| Released | Aug 7, 2025 | Jul 1, 2025 | Jun 12, 2026 |
| Knowledge cutoff | Sep 30, 2024 | Aug 22, 2024 | 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.
- GPT-5 Chat$32.50
- Jamba Large$36.00
- Kimi K2.7 Code Highspeed$35.00
Which should you choose?
Which is better: GPT-5 Chat, Jamba Large or Kimi K2.7 Code Highspeed?
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, Jamba Large or Kimi K2.7 Code Highspeed?
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, Jamba Large 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 GPT-5 Chat, Jamba Large 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. 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, Jamba Large up to 4,096, Kimi K2.7 Code Highspeed up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GPT-5 Chat accepts text and images; Jamba Large accepts text; Kimi K2.7 Code Highspeed accepts text, images and video. Kimi K2.7 Code Highspeed handles the widest range of inputs.
Are any of these open source?
Jamba Large and Kimi K2.7 Code Highspeed 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, Jamba Large Aug 22, 2024, 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.
