// head_to_head
Claude Sonnet 5.5 vs GLM 5.3 FlashX
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.
GLM 5.3 FlashX is the cheaper of the two; neither can be ranked on quality here.
GLM 5.3 FlashX 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.
anthropic
Claude Sonnet 5.5
- Blended / 1M
- $4.00
- Context
- 1M
- Released
- Sep 28, 2026
- Overall score
- 80.0
z-ai
GLM 5.3 FlashX
- Blended / 1M
- $0.590
- Context
- 1.0M
- Released
- Sep 18, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | Claude Sonnet 5.5 | GLM 5.3 FlashX |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 80.0 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0372 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.00 | $0.590win |
| Input price / 1M | $2.00 | $0.370win |
| Output price / 1M | $10.00 | $1.25win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.100 | $0.090win |
| Context window | 1M | 1.0M |
| Max output tokens | 128K | 131K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Sonnet 5.5 on top, GLM 5.3 FlashX 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 | Claude Sonnet 5.5 | GLM 5.3 FlashX |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,143/mo | $168.64/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,440/mo | $259.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,336/mo | $239.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,655/mo | $449.75/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5,050/mo | $910.00/mo |
Which should you pick?
You are cost-constrained
GLM 5.3 FlashX
Cheaper on blended list price at $0.590 per million tokens.
Claude Sonnet 5.5 vs GLM 5.3 FlashX FAQ
Which is better, Claude Sonnet 5.5 or GLM 5.3 FlashX?
GLM 5.3 FlashX is the cheaper of the two; neither can be ranked on quality here. GLM 5.3 FlashX 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 Claude Sonnet 5.5 cheaper than GLM 5.3 FlashX?
GLM 5.3 FlashX is cheaper. On a 3:1 input:output blend, Claude Sonnet 5.5 lists at $4.00 per million tokens and GLM 5.3 FlashX at $0.590 — GLM 5.3 FlashX is 6.8× cheaper. Input and output are priced separately — Claude Sonnet 5.5 charges $2.00 in and $10.00 out, GLM 5.3 FlashX charges $0.370 and $1.25 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Claude Sonnet 5.5 or GLM 5.3 FlashX have a bigger context window?
They are effectively the same — 1M for Claude Sonnet 5.5 and 1.0M for GLM 5.3 FlashX.
Do Claude Sonnet 5.5 and GLM 5.3 FlashX support prompt caching?
Both publish a cached-input rate: $0.100 per million for Claude Sonnet 5.5 and $0.090 for GLM 5.3 FlashX, against full input rates of $2.00 and $0.370. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
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 2026-10-25, 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.