Jamba Large vs Qwen MT Plus vs Qwen3 235B-A22B Thinking 2507
Qwen3 235B-A22B Thinking 2507 comes out ahead, 42 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
Alibaba (Qwen)Qwen MT Plus
11/100- ECI—
- Price$2.46 / $7.37
- Context16K
- Our pickAlibaba (Qwen)
Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
Qwen3 235B-A22B Thinking 2507 is our pick
Qwen3 235B-A22B Thinking 2507 is the better all-round choice, scoring 42/100 against Jamba Large (29) and Qwen MT Plus (11). 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 priceQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · Jamba Large $3.50 · Qwen MT Plus $3.69 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · Jamba Large 256,000 · Qwen MT Plus 16,384 tokens
- Widest inputsSame inputsJamba Large: Text · Qwen MT Plus: Text · Qwen3 235B-A22B Thinking 2507: Text
- Self-hostingJamba Large and Qwen3 235B-A22B Thinking 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | Jamba Large | Qwen MT Plus | Qwen3 235B-A22B Thinking 2507 |
|---|---|---|---|---|
| Price | 50% | 23 | 22 | 49 |
| Inputs & features | 30% | 35 | 0 | 35 |
| Context window | 20% | 36 | 0 | 37 |
| Overall | 100% | 29/100 | 11/100 | 42/100 |
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 | Qwen MT PlusAlibaba (Qwen) | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 143.8 |
| ECI rank | — | — | #93 of 165 |
| GPQA DiamondGraduate-level science questions | — | — | 80.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 86.7% |
| SimpleQA VerifiedShort factual questions | — | — | 40.4% |
| Price per million tokens | |||
| Input | $2.00 | $2.46 | $0.30 (best) |
| Output | $8.00 | $7.37 | $2.90 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.50 | $3.69 | $0.95 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official AI21 Labs API | Official Alibaba API | Median of 11 providers |
| Limits | |||
| Context window | 256,000 tokens | 16,384 tokens | 262,144 tokens (best) |
| Max output | 4,096 tokens | 8,192 tokens | 81,920 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | Proprietary | OpenApache 2.0 |
| API model ID | jamba-large | qwen-mt-plus | — |
| API providers | 1 | 3 | 12 (best) |
| Released | Jul 3, 2025 | Jul 24, 2025 | Jul 25, 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
- Qwen MT Plus$39.34
- Qwen3 235B-A22B Thinking 2507$8.80
Which should you choose?
Which is better: Jamba Large, Qwen MT Plus or Qwen3 235B-A22B Thinking 2507?
Qwen3 235B-A22B Thinking 2507 is the better all-round choice, scoring 42/100 against Jamba Large (29) and Qwen MT Plus (11). 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, Jamba Large, Qwen MT Plus or Qwen3 235B-A22B Thinking 2507?
Qwen3 235B-A22B Thinking 2507 is cheaper at $0.30 input / $2.90 output per million tokens (median across 11 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 $0.95 per million tokens for Qwen3 235B-A22B Thinking 2507 versus $3.50 for Jamba Large (3.7× as much) and $3.69 for Qwen MT Plus (3.9× 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, Qwen MT Plus has not been scored yet and Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8.
Which is better for coding?
There are no published SWE-bench Verified results for Jamba Large, Qwen MT Plus and Qwen3 235B-A22B Thinking 2507 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?
Qwen3 235B-A22B Thinking 2507 has the largest context window at 262,144 tokens, against 256,000 for Jamba Large and 16,384 for Qwen MT Plus. Maximum output per response: Jamba Large up to 4,096, Qwen MT Plus up to 8,192, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.
Which can read images, PDFs, audio or video?
Jamba Large accepts text; Qwen MT Plus accepts text; Qwen3 235B-A22B Thinking 2507 accepts text. They handle the same number of input types.
Are any of these open source?
Jamba Large and Qwen3 235B-A22B Thinking 2507 publishes its weights (Apache 2.0) and can be self-hosted; Qwen MT Plus is proprietary.
Which is newer?
Qwen3 235B-A22B Thinking 2507 is the newest, released Jul 25, 2025. Qwen MT Plus came out Jul 24, 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.
