Jamba Large vs Kimi K2 Instruct 0905
Kimi K2 Instruct 0905 comes out ahead, 38 to 29 on our weighted score, and it is the cheaper option too.
AI21 LabsJamba Large
29/100- ECI—
- Price$2.00 / $8.00
- Context256K
- Our pick
Moonshot AIKimi K2 Instruct 0905
38/100- ECI—
- Price$0.60 / $2.50
- Context262K
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Make it a three-way comparison.
Kimi K2 Instruct 0905 is our pick
Kimi K2 Instruct 0905 is the better all-round choice, scoring 38/100 against Jamba Large (29). It leads on price. Jamba Large 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 priceKimi K2 Instruct 0905Kimi K2 Instruct 0905 $1.07 · Jamba Large $3.50 per 1M tokens (3:1 blend)
- Longest contextKimi K2 Instruct 0905Kimi K2 Instruct 0905 262,144 · Jamba Large 256,000 tokens
- Widest inputsSame inputsJamba Large: Text · Kimi K2 Instruct 0905: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Jamba Large | Kimi K2 Instruct 0905 |
|---|---|---|---|
| Price | 50% | 23 | 46 |
| Inputs & features | 30% | 35 | 25 |
| Context window | 20% | 36 | 37 |
| Overall | 100% | 29/100 | 38/100 |
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 | Jamba LargeAI21 Labs | Kimi K2 Instruct 0905Moonshot AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $2.00 | $0.60 (best) |
| Output | $8.00 | $2.50 (best) |
| Cached input | — | — |
| Blended (3:1) | $3.50 | $1.07 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Official AI21 Labs API | Median of 12 providers |
| Limits | ||
| Context window | 256,000 tokens | 262,144 tokens (best) |
| Max output | 4,096 tokens | 98,304 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | Yes |
| Structured output | Yes | No |
| Availability | ||
| Weights | Open | OpenModified MIT |
| API model ID | jamba-large | — |
| API providers | 1 | 13 (best) |
| Released | Jul 3, 2025 | Sep 5, 2025 |
| Knowledge cutoff | 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.
- Jamba Large$36.00
- Kimi K2 Instruct 0905$11.00
Which should you choose?
Which is better: Jamba Large or Kimi K2 Instruct 0905?
Kimi K2 Instruct 0905 is the better all-round choice, scoring 38/100 against Jamba Large (29). It leads on price. Jamba Large 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, Jamba Large or Kimi K2 Instruct 0905?
Kimi K2 Instruct 0905 is cheaper at $0.60 input / $2.50 output per million tokens (median across 12 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 $1.07 per million tokens for Kimi K2 Instruct 0905 versus $3.50 for Jamba Large (3.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Jamba Large has not been scored yet and Kimi K2 Instruct 0905 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Jamba Large and Kimi K2 Instruct 0905 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Kimi K2 Instruct 0905 has the largest context window at 262,144 tokens, against 256,000 for Jamba Large. Maximum output per response: Jamba Large up to 4,096, Kimi K2 Instruct 0905 up to 98,304 tokens.
Which can read images, PDFs, audio or video?
Jamba Large accepts text; Kimi K2 Instruct 0905 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (Modified MIT), so you can self-host them.
Which is newer?
Kimi K2 Instruct 0905 is the newest, released Sep 5, 2025. Jamba Large came out Jul 3, 2025. Knowledge cutoff: 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 Nov 2, 2026.
