GLM-4.7 vs Jamba Large vs o4-mini-deep-research

GLM-4.7 is the better all-round choice, scoring 41/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on price. Jamba Large wins on context window. o4-mini-deep-research 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.

GLM-4.7 is cheaper at $0.60 input / $2.20 output per million tokens (official Z.AI API price). o4-mini-deep-research costs $1.80 input / $7.20 output per million tokens (median across 1 API provider); 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 $1.00 per million tokens for GLM-4.7 versus $3.15 for o4-mini-deep-research (3.1× as much) and $3.50 for Jamba Large (3.5× as much).

There is no independent benchmark that covers all three models yet. GLM-4.7 has an ECI of 143.7, Jamba Large has not been scored yet and o4-mini-deep-research has not been scored yet.

There are no published SWE-bench Verified results for GLM-4.7, Jamba Large and o4-mini-deep-research yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.

Jamba Large has the largest context window at 256,000 tokens, against 204,800 for GLM-4.7 and 200,000 for o4-mini-deep-research. Maximum output per response: GLM-4.7 up to 131,072, Jamba Large up to 4,096, o4-mini-deep-research up to 100,000 tokens.

GLM-4.7 accepts text; Jamba Large accepts text; o4-mini-deep-research accepts text and images. o4-mini-deep-research handles the widest range of inputs.

GLM-4.7 and Jamba Large publishes its weights and can be self-hosted; o4-mini-deep-research is proprietary.

GLM-4.7 is the newest, released Dec 22, 2025. Jamba Large came out Jul 3, 2025; o4-mini-deep-research came out Jun 26, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, Jamba Large Aug 22, 2024, o4-mini-deep-research May 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.