// head_to_head
Gemini 4 Argon 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.
Gemini 4 Argon scores higher, GLM 5.3 costs less — it depends on your workload.
Gemini 4 Argon is ahead by 23.4 points overall, and GLM 5.3 lists 3.2× cheaper per blended million tokens. Whether 23.4 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: GLM 5.3 has the lower sticker price, but Gemini 4 Argon earns each point of capability for less — $1.24 against $1.55 — because per-token rates do not predict how many tokens a model actually spends on a task.
Gemini 4 Argon
- Blended / 1M
- $4.00
- Context
- 1.0M
- Released
- Oct 13, 2026
- Overall score
- 84.1
z-ai
GLM 5.3
- Blended / 1M
- $1.26
- Context
- 1.0M
- Released
- Aug 18, 2026
- Overall score
- 60.7
Specs and pricing
| Metric | Gemini 4 Argon | GLM 5.3 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 84.1win | 60.7 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $1.24win | $1.55 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.00 | $1.26win |
| Input price / 1M | $2.00 | $0.079win |
| Output price / 1M | $10.00 | $4.80win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.100 | $0.059win |
| Context window | 1.0M | 1.0M |
| Max output tokens | 66K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 4 Argon 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 | Gemini 4 Argon | GLM 5.3 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,143/mo | $401.52/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,440/mo | $343.20/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,336/mo | $436.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,655/mo | $438.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5,050/mo | $667.50/mo |
Which should you pick?
You are running this at volume
Gemini 4 Argon
Lowest measured cost per point of capability at $1.24 per point — the gap compounds with every request.
Quality matters more than the bill
Gemini 4 Argon
Highest overall LiveBench score of the two at 84.1.
The workload is coding or agentic work
Gemini 4 Argon
Leads on coding — 89.7 against 82.8.
You are cost-constrained
GLM 5.3
Cheaper on blended list price at $1.26 per million tokens.
Gemini 4 Argon vs GLM 5.3 FAQ
Which is better, Gemini 4 Argon or GLM 5.3?
Gemini 4 Argon scores higher, GLM 5.3 costs less — it depends on your workload. Gemini 4 Argon is ahead by 23.4 points overall, and GLM 5.3 lists 3.2× cheaper per blended million tokens. Whether 23.4 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: GLM 5.3 has the lower sticker price, but Gemini 4 Argon earns each point of capability for less — $1.24 against $1.55 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is Gemini 4 Argon cheaper than GLM 5.3?
GLM 5.3 is cheaper. On a 3:1 input:output blend, Gemini 4 Argon lists at $4.00 per million tokens and GLM 5.3 at $1.26 — GLM 5.3 is 3.2× cheaper. Input and output are priced separately — Gemini 4 Argon charges $2.00 in and $10.00 out, GLM 5.3 charges $0.079 and $4.80 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Gemini 4 Argon vs GLM 5.3: which scores higher on benchmarks?
Gemini 4 Argon scores 84.1 and GLM 5.3 scores 60.7 overall on LiveBench, the mean of its seven categories. That is a 23.4-point lead for Gemini 4 Argon. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, Gemini 4 Argon or GLM 5.3?
Gemini 4 Argon. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Gemini 4 Argon works out at $1.24 per point and GLM 5.3 at $1.55.
Does Gemini 4 Argon or GLM 5.3 have a bigger context window?
They are effectively the same — 1.0M for Gemini 4 Argon and 1.0M for GLM 5.3.
Do Gemini 4 Argon and GLM 5.3 support prompt caching?
Both publish a cached-input rate: $0.100 per million for Gemini 4 Argon and $0.059 for GLM 5.3, against full input rates of $2.00 and $0.079. 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 2026-09-02, 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.