llama-3.3-70b-versatile vs ministral-8b-latest
ministral-8b-latest is 3.9× cheaper on input than llama-3.3-70b-versatile.
| llama-3.3-70b-versatile | ministral-8b-latest | |
|---|---|---|
| Input / 1M | $0.59 | $0.15 |
| Cached input / 1M | — | — |
| Output / 1M | $0.79 | $0.15 |
| Context window | 128K | 262K |
| Max output | 33K | 262K |
| Provider | Groq | Mistral |
| Vision | No | Yes |
| Tool calling | Yes | Yes |
| Token count | estimated | estimated |
Capability for the money
Independent measurement · Artificial Analysis · read 2026-08-11Not the same tier: llama-3.3-70b-versatile leads ministral-8b-latest by 18.0 points on following instructions (IFBench), for 4.3× the price. They are level on coding (LiveCodeBench), tool use (τ²-bench) and hard reasoning (GPQA Diamond), so the premium only pays off on following instructions work.
Following instructions
extraction, classification, sticking to a format
IFBenchllama-3.3-70b-versatile +18.0
Coding
writing and fixing code
LiveCodeBenchministral-8b-latest +1.5
Tool use
multi-step agent work against real tools
τ²-benchlevel
Hard reasoning
graduate-level science and multi-step analysis
GPQA Diamondllama-3.3-70b-versatile +2.7
Averaged over the benchmarks both models sat, ministral-8b-latest returns 221.8 points of pass rate per dollar of blended price against 59.5 — 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
- Cheaper input: ministral-8b-latest
- Cheaper output: ministral-8b-latest
- Larger context: ministral-8b-latest
- Better at following instructions: llama-3.3-70b-versatile (47.1% vs 29.1% on IFBench)
- Better at coding: ministral-8b-latest (28.8% vs 30.3% on LiveCodeBench)
- Better at tool use: llama-3.3-70b-versatile (26.6% vs 26.6% on τ²-bench)
- Better at hard reasoning: llama-3.3-70b-versatile (49.8% vs 47.1% on GPQA Diamond)
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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