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GLM 5.3 Prime vs Qwen2.5 VL 72B Instruct

Qwen2.5 VL 72B Instruct costs 8.8x less per output token than GLM 5.3 Prime ($1.00 vs $8.80 per million). Updated October 22, 2026; no benchmark has tested both models yet.

Replace GLM 5.3 Prime Replace Qwen2.5 VL 72B Instruct
GLM 5.3 PrimeZ.ai
Coding
No data
Overall
No data
Output $/M
$8.80
Qwen2.5 VL 72B InstructQwen
Coding
No data
Overall
No data
Output $/M
$1.00
vs $8.80 vs $1.00 per 1M output tokens. Qwen2.5 VL 72B Instruct is 8.8x cheaper.

GLM 5.3 Prime or Qwen2.5 VL 72B Instruct: which to pick

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

Pick GLM 5.3 Prime for a larger context window (1M against 128K tokens).

Model AGLM 5.3 PrimeZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen2.5 VL 72B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task

No benchmark has tested both yet. GLM 5.3 Prime has none yet; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. 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, Qwen2.5 VL 72B Instruct 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 Qwen2.5 VL 72B Instruct $1.00 per million output tokens, 8.8x apart.
Context window
GLM 5.3 Prime takes 1M tokens, Qwen2.5 VL 72B Instruct 128K.

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.

Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Agentic work benchmarks for GLM 5.3 Prime and Qwen2.5 VL 72B Instruct
BenchmarkGLM 5.3 PrimeQwen2.5 VL 72B InstructLeader
OSWorldNot measured5.0%Only Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 41 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Reasoning benchmarks for GLM 5.3 Prime and Qwen2.5 VL 72B Instruct
BenchmarkGLM 5.3 PrimeQwen2.5 VL 72B InstructLeader
Kagi LLM BenchmarkNot measured36.0%Only Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 22 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Knowledge benchmarks for GLM 5.3 Prime and Qwen2.5 VL 72B Instruct
BenchmarkGLM 5.3 PrimeQwen2.5 VL 72B InstructLeader
GeoBenchNot measured62.0%Only Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 19 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
General benchmarks for GLM 5.3 Prime and Qwen2.5 VL 72B Instruct
BenchmarkGLM 5.3 PrimeQwen2.5 VL 72B InstructLeader
LMArena VisionNot measured1107Only Qwen2.5 VL 72B Instruct measured
LMArena Vision: EnglishNot measured1115Only Qwen2.5 VL 72B Instruct measured
LMArena Vision: ChineseNot measured1107Only Qwen2.5 VL 72B Instruct measured
Neither model has a result on the other 45 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
Qwen2.5 VL 72B Instruct$0.0013Fits in one request

Qwen2.5 VL 72B Instruct costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Prime$0.166Fits in one request
Qwen2.5 VL 72B Instruct$0.043Fits in one request

Qwen2.5 VL 72B Instruct costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Prime$0.648Fits in one request
Qwen2.5 VL 72B Instruct$0.170Exceeds the context window

Qwen2.5 VL 72B Instruct 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 Qwen2.5 VL 72B Instruct
SpecificationZ.ai GLM 5.3 PrimeQwen2.5 VL 72B Instruct
MakerZ.aiQwen
WeightsNo public weights listedOpen weights
ReleasedSeptember 23, 2026February 1, 2025
Context window1M128K
Input $/Mtok$2.80$0.80
Output $/Mtok$8.80$1.00
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 Qwen2.5 VL 72B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 5.3 Prime for a larger context window (1M against 128K tokens).

Which is cheaper, GLM 5.3 Prime or Qwen2.5 VL 72B Instruct?

GLM 5.3 Prime costs $2.80 per million input tokens and $8.80 per million output tokens; Qwen2.5 VL 72B Instruct costs $0.80 and $1.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 5.3 Prime or Qwen2.5 VL 72B Instruct?

GLM 5.3 Prime, with 1M tokens against 128K.

Has any benchmark tested both GLM 5.3 Prime and Qwen2.5 VL 72B Instruct?

No benchmark has tested both yet. GLM 5.3 Prime has none yet; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. Compare them on price and context instead. Data as of October 22, 2026.

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