Groq vs Cerebras · synced 2026-08-11

llama-3.3-70b-versatile vs zai-glm-4.7

llama-3.3-70b-versatile is 3.8× cheaper on input than zai-glm-4.7.

llama-3.3-70b-versatile zai-glm-4.7
Input / 1M $0.59 $2.25
Cached input / 1M
Output / 1M $0.79 $2.75
Context window 128K 128K
Max output 33K 128K
Provider Groq Cerebras
Vision No No
Tool calling Yes Yes
Token count estimated estimated

Capability for the money

Independent measurement · Artificial Analysis · read 2026-08-11

Not the same tier: zai-glm-4.7 leads llama-3.3-70b-versatile by 60.6 points on coding (LiveCodeBench), for 3.7× the price.

  1. Following instructions

    extraction, classification, sticking to a format

    IFBench
    llama-3.3-70b-versatile47.1%
    zai-glm-4.767.9%

    zai-glm-4.7 +20.8

  2. Coding

    writing and fixing code

    LiveCodeBench
    llama-3.3-70b-versatile28.8%
    zai-glm-4.789.4%

    zai-glm-4.7 +60.6

  3. Tool use

    multi-step agent work against real tools

    τ²-bench
    llama-3.3-70b-versatile26.6%
    zai-glm-4.795.9%

    zai-glm-4.7 +69.3

  4. Hard reasoning

    graduate-level science and multi-step analysis

    GPQA Diamond
    llama-3.3-70b-versatile49.8%
    zai-glm-4.785.9%

    zai-glm-4.7 +36.1

Averaged over the benchmarks both models sat, llama-3.3-70b-versatile returns 59.5 points of pass rate per dollar of blended price against 35.7 — the ratio that decides whether the dearer model earns its rate.

Each figure is a pass rate: the share of that test set the model answered correctly, measured by a third party rather than self-reported by the lab. Rows are different tests of different difficulty, so compare a model against the other model, not one row against the next. And a benchmark is not your workload — it narrows the shortlist, it does not pick for you.

Which to pick

List price is not production cost. Output length, cache hit rate, retries and task quality move the real number more than the headline rate does — price your own prompt on the token ledger before deciding.

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