GLM Coding Cost (2026)

GLM-4.6 coding calls cost roughly $0.0003 each in our test, measured via OpenRouter, where one 30-token coding prompt returned 165 output tokens for $0.0002972 in 3.78 seconds; the bill is driven less by the per-token rate than by how verbose the model is when it answers. That verbosity gap is the part most coding-cost estimates miss, and it is where GLM-4.6 diverges from a leaner model like DeepSeek V4-Flash.

Coding economics are not about the headline price per million tokens. They are about how many tokens a model spends to finish your task. A model that is 20% cheaper per token but writes 40% more output to answer the same prompt costs you more per completed task. GLM-4.6 is a capable coder, but it talks. Understanding that habit is the difference between a budget that holds and one that drifts.

GLM-4.6 coding cost in practice (verified 2026-06)

GLM-4.6 is a large reasoning-capable model from Zhipu that handles code generation, refactoring, and explanation, and it tends to return longer, more annotated answers than smaller speed-tier models. That last trait is what shapes the bill.

According to Z.ai pricing, GLM-4.6 official rates are $0.60 per million input tokens and $2.20 per million output, on a 205K context window. We reached the model a different way for our own measurements: through OpenRouter, where GLM-4.6 routes at $0.43 in and $1.74 out per million. The practical upshot is that the routed price we tested sits below the official Z.ai card, the same pattern we have seen with other Chinese models where the aggregator undercuts the first-party rate. We flag this asymmetry rather than smooth over it: the official numbers above still need native-key re-verification.

Source	Input $/1M	Output $/1M	How we cite it
GLM-4.6 (measured via OpenRouter)	$0.43	$1.74	first-hand routed price
GLM-4.6 (official Z.ai)	$0.60	$2.20	per Z.ai, needs re-verify
DeepSeek V4-Flash (official)	$0.14	$0.28	per DeepSeek docs

The output column is where coding bills are won or lost. GLM-4.6's output rate, whether $1.74 routed or $2.20 official, is several times DeepSeek V4-Flash's $0.28. And output is exactly the side a verbose coder inflates.

We measured a coding call (first-hand evidence, via OpenRouter)

Documentation gives you a rate card. It does not tell you how many tokens a model actually spends to finish a coding task, so we called the endpoints and watched the meter.

When we sent GLM-4.6 a coding prompt through OpenRouter, the request was 30 input tokens and the model answered with 165 output tokens. That single call billed $0.0002972 and took 3.78 seconds. We then ran the same style of coding prompt against DeepSeek V4-Flash on its official api.deepseek.com endpoint: 27 input tokens in, 120 output tokens out, returned in 2.25 seconds. Note the asymmetry we are disclosing on purpose: GLM-4.6 here is OpenRouter-routed, while the DeepSeek figure is from the native official API.

Model (routing)	Input tokens	Output tokens	Latency	Billed cost
GLM-4.6 (via OpenRouter)	30	165	3.78s	$0.0002972
DeepSeek V4-Flash (official)	27	120	2.25s	not billed in test

The number that jumps out is 165 versus 120. For comparable coding prompts, GLM-4.6 produced about 38% more output tokens than DeepSeek V4-Flash. That is not noise; it is the model's answering style. GLM-4.6 explains, comments, and elaborates, where V4-Flash tends to return tighter code. On the output side of the bill, that 38% gap compounds with GLM-4.6's higher per-token output rate.

Why verbosity, not the rate card, sets your coding bill

Here is the trap. You compare two rate cards, pick the cheaper per-token model, and budget from there. But the per-task cost is rate multiplied by tokens spent, and a chatty model spends more tokens.

Take our measured token counts and price each call at the output rate that applies. GLM-4.6's 165 output tokens at its routed $1.74 per million come to about $0.000287 in output alone. DeepSeek V4-Flash's 120 output tokens at its official $0.28 per million come to about $0.0000336. That is roughly an 8.5x difference on output for a single comparable coding task, and only part of it is the per-token rate. The rest is the extra 45 tokens GLM-4.6 chose to write.

At one call this is fractions of a cent. At a million coding calls a month, the verbosity multiplier is the line item that decides your invoice. The fix is not abandoning GLM-4.6; it is constraining it. Set a tighter max_tokens ceiling, ask for code without prose explanation when you do not need it, and you claw back the tokens that drove the gap.

According to DeepSeek API Docs, V4-Flash bills output at $0.28 per million, which is the floor any verbose alternative is measured against. The reason GLM-4.6 can still win a task is quality: if its longer answer is correct on the first try and a leaner model needs a second round-trip, the verbose call was the cheaper path. Per-task cost has to be read against per-task success, not in isolation.

When GLM-4.6 is worth the extra tokens

Choose GLM-4.6 for coding when answer quality and reasoning depth matter more than raw per-task cost, and when its longer outputs reduce your retry rate. Its 3.78-second latency in our test was faster than several of its sibling tiers, so speed is not the reason to avoid it.

Avoid GLM-4.6, or cap it hard, for high-volume, simple, repetitive coding calls where a terser model finishes the same task in fewer tokens. According to Z.ai pricing, Zhipu also offers a free Flash tier for lighter models, which is worth testing for low-stakes coding tasks before you pay for GLM-4.6's output. For straight cost-per-token on simple generation, DeepSeek V4-Flash remains the leaner option. The deciding question is always the same: does GLM-4.6's verbosity buy you fewer retries, or just a bigger bill?

For the full rate card across every GLM tier, see the GLM API pricing hub. For the DeepSeek comparison anchor, see the DeepSeek API pricing hub.

FAQ

How much does a GLM-4.6 coding call cost? In our first-hand test, measured via OpenRouter, one coding call of 30 input and 165 output tokens billed $0.0002972 and took 3.78 seconds. Your cost scales with how many output tokens the model writes, which for GLM-4.6 tends to run high.

Is GLM-4.6 more expensive than DeepSeek V4-Flash for coding? On output rate, yes. GLM-4.6 routes at $1.74 per million output via OpenRouter ($2.20 official per Z.ai) versus DeepSeek V4-Flash's $0.28 official. GLM-4.6 also produced about 38% more output tokens on a comparable coding prompt in our test, which widens the gap further.

Why does GLM-4.6 cost more per coding task even at a low per-token price? Because per-task cost is the rate times the tokens spent, and GLM-4.6 is verbose. In our test it wrote 165 output tokens to DeepSeek V4-Flash's 120 for similar prompts. The extra tokens, billed at GLM-4.6's higher output rate, are what inflate the per-task bill.

Can I reduce GLM-4.6's coding cost? Yes. Cap max_tokens, request code without prose explanation when you do not need it, and reserve GLM-4.6 for tasks where its longer answers cut your retry rate. The free Flash tier on Z.ai is worth testing first for low-stakes coding.

Are these GLM-4.6 prices official Z.ai rates? No. Our measured numbers are OpenRouter-routed. The official Z.ai card ($0.60 in / $2.20 out) is cited separately and still needs native-key re-verification.

This is part of the GLM API pricing hub.

Author: Kevin Fan, Customer Success Manager at China LLM Directory, specializing in Chinese LLM ecosystem pricing and model benchmarking. Last verified: 2026-06-26.

GLM-4.6 coding calls cost roughly $0.0003 each in our test, measured via OpenRouter, where one 30-token coding prompt returned 165 output tokens for…

Written by Fan Chuanyu

GLM-4.6 coding cost in practice (verified 2026-06)

We measured a coding call (first-hand evidence, via OpenRouter)

Why verbosity, not the rate card, sets your coding bill

When GLM-4.6 is worth the extra tokens

FAQ