Jamba Large vs Qwen3 235B-A22B Thinking 2507 vs Qwen MT Plus

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.

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).

There is no independent benchmark that covers all three models yet. Jamba Large has not been scored yet, Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8 and Qwen MT Plus has not been scored yet.

There are no published SWE-bench Verified results for Jamba Large, Qwen3 235B-A22B Thinking 2507 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.

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, Qwen3 235B-A22B Thinking 2507 up to 81,920, Qwen MT Plus up to 8,192 tokens.

Jamba Large accepts text; Qwen3 235B-A22B Thinking 2507 accepts text; Qwen MT Plus accepts text. They handle the same number of input types.

Jamba Large and Qwen3 235B-A22B Thinking 2507 publishes its weights (Apache 2.0) and can be self-hosted; Qwen MT Plus is proprietary.

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.

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.