Jamba Large vs Gemini 2.5 Pro Preview 06-05 vs Qwen MT Plus
Gemini 2.5 Pro Preview 06-05 comes out ahead, 48 to 29 and 11 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
GoogleGemini 2.5 Pro Preview 06-05
48/100- ECI—
- Price$1.22 / $9.75
- Context1.05M
Alibaba (Qwen)Qwen MT Plus
11/100- ECI—
- Price$2.46 / $7.37
- Context16K
Gemini 2.5 Pro Preview 06-05 is our pick
Gemini 2.5 Pro Preview 06-05 is the better all-round choice, scoring 48/100 against Jamba Large (29) and Qwen MT Plus (11). It leads on inputs & features and 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 priceGemini 2.5 Pro Preview 06-05Gemini 2.5 Pro Preview 06-05 $3.35 · Jamba Large $3.50 · Qwen MT Plus $3.69 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 Pro Preview 06-05Gemini 2.5 Pro Preview 06-05 1,048,576 · Jamba Large 256,000 · Qwen MT Plus 16,384 tokens
- Widest inputsGemini 2.5 Pro Preview 06-05Jamba Large: Text · Gemini 2.5 Pro Preview 06-05: Text, Images, PDFs, Audio · Qwen MT Plus: Text
- Self-hostingJamba LargePublishes downloadable weights
| Measure | Weight | Jamba Large | Gemini 2.5 Pro Preview 06-05 | Qwen MT Plus |
|---|---|---|---|---|
| Price | 50% | 23 | 24 | 22 |
| Inputs & features | 30% | 35 | 80 | 0 |
| Context window | 20% | 36 | 61 | 0 |
| Overall | 100% | 29/100 | 48/100 | 11/100 |
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 | Gemini 2.5 Pro Preview 06-05Google | Qwen MT PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $2.00 | $1.22 (best) | $2.46 |
| Output | $8.00 | $9.75 | $7.37 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.50 | $3.35 (best) | $3.69 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official AI21 Labs API | Median of 4 providers | Official Alibaba API |
| Limits | |||
| Context window | 256,000 tokens | 1,048,576 tokens (best) | 16,384 tokens |
| Max output | 4,096 tokens | 65,536 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | No | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | No |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | jamba-large | — | qwen-mt-plus |
| API providers | 1 | 4 (best) | 3 |
| Released | Jul 3, 2025 | Jun 5, 2025 | Jul 24, 2025 |
| Knowledge cutoff | Aug 22, 2024 | Jan 31, 2025 | — |
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
- Gemini 2.5 Pro Preview 06-05$31.69
- Qwen MT Plus$39.34
Which should you choose?
Which is better: Jamba Large, Gemini 2.5 Pro Preview 06-05 or Qwen MT Plus?
Gemini 2.5 Pro Preview 06-05 is the better all-round choice, scoring 48/100 against Jamba Large (29) and Qwen MT Plus (11). It leads on inputs & features and 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, Jamba Large, Gemini 2.5 Pro Preview 06-05 or Qwen MT Plus?
Gemini 2.5 Pro Preview 06-05 is cheaper at $1.22 input / $9.75 output per million tokens (median across 4 API providers). Jamba Large costs $2.00 input / $8.00 output per million tokens (official AI21 Labs API price); Qwen MT Plus costs $2.46 input / $7.37 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $3.35 per million tokens for Gemini 2.5 Pro Preview 06-05 versus $3.50 for Jamba Large (1× as much) and $3.69 for Qwen MT Plus (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Jamba Large has not been scored yet, Gemini 2.5 Pro Preview 06-05 has not been scored yet and Qwen MT Plus has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Jamba Large, Gemini 2.5 Pro Preview 06-05 and Qwen MT Plus yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen MT Plus does not support tool calling, which most coding agents need.
Which has the bigger context window?
Gemini 2.5 Pro Preview 06-05 has the largest context window at 1,048,576 tokens, against 256,000 for Jamba Large and 16,384 for Qwen MT Plus. Maximum output per response: Jamba Large up to 4,096, Gemini 2.5 Pro Preview 06-05 up to 65,536, Qwen MT Plus up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Jamba Large accepts text; Gemini 2.5 Pro Preview 06-05 accepts text, images, PDFs and audio; Qwen MT Plus accepts text. Gemini 2.5 Pro Preview 06-05 handles the widest range of inputs.
Are any of these open source?
Jamba Large publishes its weights and can be self-hosted; Gemini 2.5 Pro Preview 06-05 and Qwen MT Plus is proprietary.
Which is newer?
Qwen MT Plus is the newest, released Jul 24, 2025. Jamba Large came out Jul 3, 2025; Gemini 2.5 Pro Preview 06-05 came out Jun 5, 2025. Knowledge cutoff: Jamba Large Aug 22, 2024, Gemini 2.5 Pro Preview 06-05 Jan 31, 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 Nov 2, 2026.
