// head_to_head
GPT-6 Sol vs GLM 5.3 Flash
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.
GPT-6 Sol scores higher, GLM 5.3 Flash costs less — it depends on your workload.
GPT-6 Sol is ahead by 11.3 points overall, and GLM 5.3 Flash lists 16× cheaper per blended million tokens. Whether 11.3 points is worth that depends on how much a wrong answer costs you. GLM 5.3 Flash also leads on measured cost per point of capability, at $0.1008 per point.
openai
GPT-6 Sol
- Blended / 1M
- $4.00
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- 81.9
z-ai
GLM 5.3 Flash
- Blended / 1M
- $0.245
- Context
- 1.0M
- Released
- Aug 26, 2026
- Overall score
- 70.6
Specs and pricing
| Metric | GPT-6 Sol | GLM 5.3 Flash |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 81.9win | 70.6 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.7438 | $0.1008win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.00 | $0.245win |
| Input price / 1M | $2.00 | $0.026win |
| Output price / 1M | $10.00 | $0.900win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200 | $0.011win |
| Context window | 1.1M | 1.0M |
| Max output tokens | 128K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-6 Sol on top, GLM 5.3 Flash 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 | GPT-6 Sol | GLM 5.3 Flash |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,150/mo | $77.16/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,480/mo | $68.76/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,392/mo | $84.31/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,660/mo | $92.83/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5,100/mo | $147.60/mo |
Which should you pick?
You are running this at volume
GLM 5.3 Flash
Lowest measured cost per point of capability at $0.1008 per point — the gap compounds with every request.
Quality matters more than the bill
GPT-6 Sol
Highest overall LiveBench score of the two at 81.9.
The workload is coding or agentic work
GPT-6 Sol
Leads on coding — 93.1 against 80.6.
GPT-6 Sol vs GLM 5.3 Flash FAQ
Which is better, GPT-6 Sol or GLM 5.3 Flash?
GPT-6 Sol scores higher, GLM 5.3 Flash costs less — it depends on your workload. GPT-6 Sol is ahead by 11.3 points overall, and GLM 5.3 Flash lists 16× cheaper per blended million tokens. Whether 11.3 points is worth that depends on how much a wrong answer costs you. GLM 5.3 Flash also leads on measured cost per point of capability, at $0.1008 per point.
Is GPT-6 Sol cheaper than GLM 5.3 Flash?
GLM 5.3 Flash is cheaper. On a 3:1 input:output blend, GPT-6 Sol lists at $4.00 per million tokens and GLM 5.3 Flash at $0.245 — GLM 5.3 Flash is 16× cheaper. Input and output are priced separately — GPT-6 Sol charges $2.00 in and $10.00 out, GLM 5.3 Flash charges $0.026 and $0.900 — so the model that looks cheaper flips depending on how output-heavy your workload is.
GPT-6 Sol vs GLM 5.3 Flash: which scores higher on benchmarks?
GPT-6 Sol scores 81.9 and GLM 5.3 Flash scores 70.6 overall on LiveBench, the mean of its seven categories. That is a 11.3-point lead for GPT-6 Sol. 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, GPT-6 Sol or GLM 5.3 Flash?
GLM 5.3 Flash. 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. GPT-6 Sol works out at $0.7438 per point and GLM 5.3 Flash at $0.1008.
Does GPT-6 Sol or GLM 5.3 Flash have a bigger context window?
They are effectively the same — 1.1M for GPT-6 Sol and 1.0M for GLM 5.3 Flash.
Do GPT-6 Sol and GLM 5.3 Flash support prompt caching?
Both publish a cached-input rate: $0.200 per million for GPT-6 Sol and $0.011 for GLM 5.3 Flash, against full input rates of $2.00 and $0.026. 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.