// head_to_head
Kimi K2.7 Code 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.
GLM 5.3 Flash wins outright — it scores higher and costs less.
GLM 5.3 Flash leads by 3.2 points overall while listing 11× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. GLM 5.3 Flash also leads on measured cost per point of capability, at $0.0161 per point.
moonshotai
Kimi K2.7 Code
- Blended / 1M
- $1.35
- Context
- 262K
- Released
- Jun 12, 2026
- Overall score
- 68.4
z-ai
GLM 5.3 Flash
- Blended / 1M
- $0.119
- Context
- 1.3M
- Released
- Aug 26, 2026
- Overall score
- 71.6
Specs and pricing
| Metric | Kimi K2.7 Code | GLM 5.3 Flash |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 68.4 | 71.6win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0545 | $0.0161win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.35 | $0.119win |
| Input price / 1M | $0.670 | $0.075win |
| Output price / 1M | $3.40 | $0.250win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.190 | $0.015win |
| Context window | 262K | 1.3Mwin |
| Max output tokens | 236Kwin | 131K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Kimi K2.7 Code 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 | Kimi K2.7 Code | GLM 5.3 Flash |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $398.24/mo | $33.68/mo |
| RAG assistant 8K in / 600 out × 100K requests | $548.00/mo | $51.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $539.20/mo | $46.40/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $901.00/mo | $90.75/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1775.00/mo | $182.50/mo |
Which should you pick?
You are running this at volume
GLM 5.3 Flash
Lowest measured cost per point of capability at $0.0161 per point — the gap compounds with every request.
Quality matters more than the bill
GLM 5.3 Flash
Highest overall LiveBench score of the two at 71.6.
The workload is coding or agentic work
GLM 5.3 Flash
Leads on agentic coding — 56.8 against 45.7.
You need to fit large documents in one call
GLM 5.3 Flash
Wider context window — 1.3M against 262K.
Kimi K2.7 Code vs GLM 5.3 Flash FAQ
Which is better, Kimi K2.7 Code or GLM 5.3 Flash?
GLM 5.3 Flash wins outright — it scores higher and costs less. GLM 5.3 Flash leads by 3.2 points overall while listing 11× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. GLM 5.3 Flash also leads on measured cost per point of capability, at $0.0161 per point.
Is Kimi K2.7 Code cheaper than GLM 5.3 Flash?
GLM 5.3 Flash is cheaper. On a 3:1 input:output blend, Kimi K2.7 Code lists at $1.35 per million tokens and GLM 5.3 Flash at $0.119 — GLM 5.3 Flash is 11× cheaper. Input and output are priced separately — Kimi K2.7 Code charges $0.670 in and $3.40 out, GLM 5.3 Flash charges $0.075 and $0.250 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Kimi K2.7 Code vs GLM 5.3 Flash: which scores higher on benchmarks?
Kimi K2.7 Code scores 68.4 and GLM 5.3 Flash scores 71.6 overall on LiveBench, the mean of its seven categories. That is a 3.2-point lead for GLM 5.3 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, Kimi K2.7 Code 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. Kimi K2.7 Code works out at $0.0545 per point and GLM 5.3 Flash at $0.0161.
Does Kimi K2.7 Code or GLM 5.3 Flash have a bigger context window?
GLM 5.3 Flash has the larger context window: 262K for Kimi K2.7 Code against 1.3M for GLM 5.3 Flash. 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 Kimi K2.7 Code and GLM 5.3 Flash support prompt caching?
Both publish a cached-input rate: $0.190 per million for Kimi K2.7 Code and $0.015 for GLM 5.3 Flash, against full input rates of $0.670 and $0.075. 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 2026-06-25, 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.