// head_to_head
MiniMax M3.1 Flash 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.
MiniMax M3.1 Flash scores higher, GLM 5.3 Flash costs less — it depends on your workload.
MiniMax M3.1 Flash is ahead by 4.8 points overall, and GLM 5.3 Flash lists 11% cheaper per blended million tokens. Whether 4.8 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 Flash has the lower sticker price, but MiniMax M3.1 Flash earns each point of capability for less — $0.0015 against $0.0018 — because per-token rates do not predict how many tokens a model actually spends on a task.
minimax
MiniMax M3.1 Flash
- Blended / 1M
- $0.262
- Context
- 1.0M
- Released
- Oct 19, 2026
- Overall score
- 66.9
z-ai
GLM 5.3 Flash
- Blended / 1M
- $0.237
- Context
- 1.0M
- Released
- Aug 26, 2026
- Overall score
- 62.1
Specs and pricing
| Metric | MiniMax M3.1 Flash | GLM 5.3 Flash |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 66.9win | 62.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0015win | $0.0018 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.262 | $0.237win |
| Input price / 1M | $0.150 | $0.150 |
| Output price / 1M | $0.600 | $0.500win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.015win | $0.030 |
| Context window | 1.0M | 1.0M |
| Max output tokens | 524K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — MiniMax M3.1 Flash 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 | MiniMax M3.1 Flash | GLM 5.3 Flash |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $74.28/mo | $67.36/mo |
| RAG assistant 8K in / 600 out × 100K requests | $102.00/mo | $102.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $92.40/mo | $92.80/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $188.25/mo | $181.50/mo |
| Bulk classification 500 in / 20 out × 5M requests | $367.50/mo | $365.00/mo |
Which should you pick?
You are running this at volume
MiniMax M3.1 Flash
Lowest measured cost per point of capability at $0.0015 per point — the gap compounds with every request.
Quality matters more than the bill
MiniMax M3.1 Flash
Highest overall LiveBench score of the two at 66.9.
The workload is coding or agentic work
MiniMax M3.1 Flash
Leads on long-horizon agentic — 28.8 against 19.0.
You are cost-constrained
GLM 5.3 Flash
Cheaper on blended list price at $0.237 per million tokens.
MiniMax M3.1 Flash vs GLM 5.3 Flash FAQ
Which is better, MiniMax M3.1 Flash or GLM 5.3 Flash?
MiniMax M3.1 Flash scores higher, GLM 5.3 Flash costs less — it depends on your workload. MiniMax M3.1 Flash is ahead by 4.8 points overall, and GLM 5.3 Flash lists 11% cheaper per blended million tokens. Whether 4.8 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 Flash has the lower sticker price, but MiniMax M3.1 Flash earns each point of capability for less — $0.0015 against $0.0018 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is MiniMax M3.1 Flash cheaper than GLM 5.3 Flash?
GLM 5.3 Flash is cheaper. On a 3:1 input:output blend, MiniMax M3.1 Flash lists at $0.262 per million tokens and GLM 5.3 Flash at $0.237 — GLM 5.3 Flash is 11% cheaper. Input and output are priced separately — MiniMax M3.1 Flash charges $0.150 in and $0.600 out, GLM 5.3 Flash charges $0.150 and $0.500 — so the model that looks cheaper flips depending on how output-heavy your workload is.
MiniMax M3.1 Flash vs GLM 5.3 Flash: which scores higher on benchmarks?
MiniMax M3.1 Flash scores 66.9 and GLM 5.3 Flash scores 62.1 overall on LiveBench, the weighted mean of its seven categories. That is a 4.8-point lead for MiniMax M3.1 Flash. 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, MiniMax M3.1 Flash or GLM 5.3 Flash?
MiniMax M3.1 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. MiniMax M3.1 Flash works out at $0.0015 per point and GLM 5.3 Flash at $0.0018.
Does MiniMax M3.1 Flash or GLM 5.3 Flash have a bigger context window?
They are effectively the same — 1.0M for MiniMax M3.1 Flash and 1.0M for GLM 5.3 Flash.
Do MiniMax M3.1 Flash and GLM 5.3 Flash support prompt caching?
Both publish a cached-input rate: $0.015 per million for MiniMax M3.1 Flash and $0.030 for GLM 5.3 Flash, against full input rates of $0.150 and $0.150. 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.