Command A Vision vs GPT-5 Chat vs Jamba Large

GPT-5 Chat is the better all-round choice, scoring 34/100 against Jamba Large (30) and Command A Vision (22). 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.

GPT-5 Chat is cheaper at $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); Command A Vision costs $2.50 input / $10.00 output per million tokens (official Cohere API price). At a typical mix of three input tokens to one output token, that is $3.44 per million tokens for GPT-5 Chat versus $3.50 for Jamba Large (1× as much) and $4.38 for Command A Vision (1.3× as much).

There is no independent benchmark that covers all three models yet. Command A Vision has not been scored yet, GPT-5 Chat has not been scored yet and Jamba Large has not been scored yet.

There are no published SWE-bench Verified results for Command A Vision, GPT-5 Chat and Jamba Large yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Command A Vision and GPT-5 Chat does not support tool calling, which most coding agents need.

GPT-5 Chat has the largest context window at 400,000 tokens, against 256,000 for Jamba Large and 128,000 for Command A Vision. Maximum output per response: Command A Vision up to 8,000, GPT-5 Chat up to 128,000, Jamba Large up to 4,096 tokens.

Command A Vision accepts text and images; GPT-5 Chat accepts text and images; Jamba Large accepts text. Command A Vision handles the widest range of inputs.

Command A Vision and Jamba Large publishes its weights and can be self-hosted; GPT-5 Chat is proprietary.

GPT-5 Chat is the newest, released Aug 7, 2025. Command A Vision came out Jul 31, 2025; Jamba Large came out Jul 1, 2025. Knowledge cutoff: Command A Vision Jun 1, 2024, GPT-5 Chat Sep 30, 2024, 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 Oct 4, 2026.