// head_to_head
Llama 3.1 Euryale 70B v2.2 vs GLM 5.3
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Llama 3.1 Euryale 70B v2.2 is the cheaper of the two; neither can be ranked on quality here.
Llama 3.1 Euryale 70B v2.2 does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
sao10k
Llama 3.1 Euryale 70B v2.2
- Blended / 1M
- $0.850
- Context
- 131K
- Released
- Aug 28, 2024
- Overall score
- Not evaluated
z-ai
GLM 5.3
- Blended / 1M
- $1.29
- Context
- 1.3M
- Released
- Aug 18, 2026
- Overall score
- 76.1
Specs and pricing
| Metric | Llama 3.1 Euryale 70B v2.2 | GLM 5.3 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 76.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.2460 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.850win | $1.29 |
| Input price / 1M | $0.850 | $0.840 |
| Output price / 1M | $0.850win | $2.64 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | $0.156 |
| Context window | 131K | 1.3Mwin |
| Max output tokens | 16K | 131Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Llama 3.1 Euryale 70B v2.2 on top, GLM 5.3 below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Llama 3.1 Euryale 70B v2.2 | GLM 5.3 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $272.00/mo | $363.55/mo |
| RAG assistant 8K in / 600 out × 100K requests | $731.00/mo | $556.80/mo |
| Coding agent 40K in / 4K out × 20K requests | $748.00/mo | $500.16/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $913.75/mo | $1,004/mo |
| Bulk classification 500 in / 20 out × 5M requests | $2,210/mo | $2,022/mo |
Which should you pick?
You need to fit large documents in one call
GLM 5.3
Wider context window — 1.3M against 131K.
You are cost-constrained
Llama 3.1 Euryale 70B v2.2
Cheaper on blended list price at $0.850 per million tokens.
Llama 3.1 Euryale 70B v2.2 vs GLM 5.3 FAQ
Which is better, Llama 3.1 Euryale 70B v2.2 or GLM 5.3?
Llama 3.1 Euryale 70B v2.2 is the cheaper of the two; neither can be ranked on quality here. Llama 3.1 Euryale 70B v2.2 does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is Llama 3.1 Euryale 70B v2.2 cheaper than GLM 5.3?
Llama 3.1 Euryale 70B v2.2 is cheaper. On a 3:1 input:output blend, Llama 3.1 Euryale 70B v2.2 lists at $0.850 per million tokens and GLM 5.3 at $1.29 — Llama 3.1 Euryale 70B v2.2 is 1.5× cheaper. Input and output are priced separately — Llama 3.1 Euryale 70B v2.2 charges $0.850 in and $0.850 out, GLM 5.3 charges $0.840 and $2.64 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Llama 3.1 Euryale 70B v2.2 or GLM 5.3 have a bigger context window?
GLM 5.3 has the larger context window: 131K for Llama 3.1 Euryale 70B v2.2 against 1.3M for GLM 5.3. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do Llama 3.1 Euryale 70B v2.2 and GLM 5.3 support prompt caching?
GLM 5.3 publishes a cached-input rate of $0.156 per million tokens against a full input rate of $0.840. The catalogue lists no separate cached rate for Llama 3.1 Euryale 70B v2.2, which means the provider does not price it separately here — not that caching is unavailable.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.