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GLM 5.3 Prime vs Mercury 3

Mercury 3 costs 25.1x less per output token than GLM 5.3 Prime ($0.35 vs $8.80 per million). Updated October 22, 2026; no benchmark has tested both models yet.

GLM 5.3 PrimeZ.ai
Coding
No data
Overall
No data
Output $/M
$8.80
Mercury 3Inception
Coding
No data
Overall
No data
Output $/M
$0.35
vs $8.80 vs $0.35 per 1M output tokens. Mercury 3 is 25.1x cheaper.

GLM 5.3 Prime or Mercury 3: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

No benchmark has tested both yet. GLM 5.3 Prime has none yet; Mercury 3 on Design Arena: websites, Design Arena: UI components and Design Arena: game dev. Compare them on price and context instead.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GLM 5.3 Prime not benchmarked, Mercury 3 not benchmarked.
Widest category
None yet: no category has been tested on both models.
Widest single gap
None yet: no benchmark has tested both models.
Output price
GLM 5.3 Prime $8.80 against Mercury 3 $0.35 per million output tokens, 25.1x apart.
Context window
Both take 1M tokens.

Benchmark by benchmark

Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.

Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
Web design benchmarks for GLM 5.3 Prime and Mercury 3
BenchmarkGLM 5.3 PrimeMercury 3Leader
Design Arena: websitesNot measured1242Only Mercury 3 measured
Design Arena: UI componentsNot measured1255Only Mercury 3 measured
Design Arena: game devNot measured1225Only Mercury 3 measured
Design Arena: data vizNot measured1192Only Mercury 3 measured
Design Arena: all code categoriesNot measured1237Only Mercury 3 measured
Neither model has a result on the other 4 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GLM 5.3 Prime$0.0072Fits in one request
Mercury 3$0.0003Fits in one request

Mercury 3 costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Prime$0.166Fits in one request
Mercury 3$0.0060Fits in one request

Mercury 3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Prime$0.648Fits in one request
Mercury 3$0.024Fits in one request

Mercury 3 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GLM 5.3 Prime and Mercury 3
SpecificationZ.ai GLM 5.3 PrimeInception Mercury 3
MakerZ.aiInception
WeightsNo public weights listedNo public weights listed
ReleasedSeptember 23, 2026October 16, 2026
Context window1M1M
Input $/Mtok$2.80$0.10
Output $/Mtok$8.80$0.35
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 Prime better than Mercury 3 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, GLM 5.3 Prime or Mercury 3?

GLM 5.3 Prime costs $2.80 per million input tokens and $8.80 per million output tokens; Mercury 3 costs $0.10 and $0.35. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 5.3 Prime or Mercury 3?

Neither: both take 1M tokens.

Has any benchmark tested both GLM 5.3 Prime and Mercury 3?

No benchmark has tested both yet. GLM 5.3 Prime has none yet; Mercury 3 on Design Arena: websites, Design Arena: UI components and Design Arena: game dev. Compare them on price and context instead. Data as of October 22, 2026.

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