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GLM 5.3 Prime vs o1-pro

Updated October 21, 2026. No benchmark has tested both models yet. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

GLM 5.3 PrimeZ.ai
Coding
No data
Overall
No data
Output $/M
$8.80
o1-proOpenAI
Coding
No data
Overall
12.4
Output $/M
$600.00
vs $8.80 vs $600.00 per 1M output tokens. GLM 5.3 Prime is 68.2x cheaper.

Decision reading

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

GLM 5.3 Prime has the edge where output tokens cost 68.2x less, and where the context window is larger, 1M against 200K tokens.

No benchmark has tested both yet. GLM 5.3 Prime has none yet; o1-pro on AA Intelligence Index and ARC-AGI. 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, o1-pro 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 o1-pro $600.00 per million output tokens, 68.2x apart.
Context window
GLM 5.3 Prime takes 1M tokens, o1-pro 200K.

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.

IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Intelligence benchmarks for GLM 5.3 Prime and o1-pro
BenchmarkGLM 5.3 Primeo1-proLeader
AA Intelligence IndexNot measured12.4Only o1-pro measured
Neither model has a result on the other 1 board here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Reasoning benchmarks for GLM 5.3 Prime and o1-pro
BenchmarkGLM 5.3 Primeo1-proLeader
ARC-AGINot measured23.3%Only o1-pro measured
Neither model has a result on the other 20 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
o1-pro$0.450Fits in one request

GLM 5.3 Prime costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Prime$0.166Fits in one request
o1-pro$9.30Fits in one request

GLM 5.3 Prime costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Prime$0.648Fits in one request
o1-pro$36.00Exceeds the context window

GLM 5.3 Prime 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 o1-pro
SpecificationZ.ai GLM 5.3 PrimeOpenAI o1-pro
MakerZ.aiOpenAI
WeightsNo public weights listedNo public weights listed
ReleasedSeptember 23, 2026March 19, 2025
Context window1M200K
Input $/Mtok$2.80$150.00
Output $/Mtok$8.80$600.00
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 o1-pro for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 5.3 Prime has the edge where output tokens cost 68.2x less, and where the context window is larger, 1M against 200K tokens.

Which is cheaper, GLM 5.3 Prime or o1-pro?

GLM 5.3 Prime costs $2.80 per million input tokens and $8.80 per million output tokens; o1-pro costs $150.00 and $600.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 5.3 Prime or o1-pro?

GLM 5.3 Prime, with 1M tokens against 200K.

Has any benchmark tested both GLM 5.3 Prime and o1-pro?

No benchmark has tested both yet. GLM 5.3 Prime has none yet; o1-pro on AA Intelligence Index and ARC-AGI. Compare them on price and context instead. Data as of October 21, 2026.

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