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GLM 5.3 Prime vs Qwen2.5 7B Instruct

Qwen2.5 7B Instruct costs 44.0x less per output token than GLM 5.3 Prime ($0.20 vs $8.80 per million). Updated October 22, 2026; no benchmark has tested both models yet.

Replace GLM 5.3 Prime Replace Qwen2.5 7B Instruct
GLM 5.3 PrimeZ.ai
Coding
No data
Overall
No data
Output $/M
$8.80
Qwen2.5 7B InstructQwen
Coding
No data
Overall
No data
Output $/M
$0.20
vs $8.80 vs $0.20 per 1M output tokens. Qwen2.5 7B Instruct is 44.0x cheaper.

GLM 5.3 Prime or Qwen2.5 7B 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 33K tokens).

Model AGLM 5.3 PrimeZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen2.5 7B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task

No benchmark has tested both yet. GLM 5.3 Prime has none yet; Qwen2.5 7B Instruct on Epoch Capabilities Index, BALROG and GPQA Diamond (Epoch AI run). 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 7B 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 7B Instruct $0.20 per million output tokens, 44.0x apart.
Context window
GLM 5.3 Prime takes 1M tokens, Qwen2.5 7B Instruct 33K.

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 Qwen2.5 7B Instruct
BenchmarkGLM 5.3 PrimeQwen2.5 7B InstructLeader
Epoch Capabilities IndexNot measured118.5Only Qwen2.5 7B Instruct measured
Neither model has a result on the other 1 board here
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 7B Instruct
BenchmarkGLM 5.3 PrimeQwen2.5 7B InstructLeader
BALROGNot measured7.8%Only Qwen2.5 7B Instruct measured
Neither model has a result on the other 40 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 7B Instruct
BenchmarkGLM 5.3 PrimeQwen2.5 7B InstructLeader
GPQA Diamond (Epoch AI run)Not measured35.5%Only Qwen2.5 7B Instruct measured
Neither model has a result on the other 21 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Math benchmarks for GLM 5.3 Prime and Qwen2.5 7B Instruct
BenchmarkGLM 5.3 PrimeQwen2.5 7B InstructLeader
OTIS Mock AIMENot measured2.5%Only Qwen2.5 7B Instruct measured
Neither model has a result on the other 12 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 7B Instruct
BenchmarkGLM 5.3 PrimeQwen2.5 7B InstructLeader
MMLUNot measured72.9%Only Qwen2.5 7B Instruct measured
Neither model has a result on the other 17 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
General benchmarks for GLM 5.3 Prime and Qwen2.5 7B Instruct
BenchmarkGLM 5.3 PrimeQwen2.5 7B InstructLeader
DTBenchNot measured47.7%Only Qwen2.5 7B Instruct measured
LMCANot measured6.4%Only Qwen2.5 7B Instruct measured
Neither model has a result on the other 44 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 7B Instruct$0.0002Fits in one request

Qwen2.5 7B Instruct costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Prime$0.166Fits in one request
Qwen2.5 7B Instruct$0.0056Exceeds the context window

Qwen2.5 7B Instruct costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Prime$0.648Fits in one request
Qwen2.5 7B Instruct$0.022Exceeds the context window

Qwen2.5 7B 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 7B Instruct
SpecificationZ.ai GLM 5.3 PrimeQwen2.5 7B Instruct
MakerZ.aiQwen
WeightsNo public weights listedOpen weights
ReleasedSeptember 23, 2026October 16, 2024
Context window1M33K
Input $/Mtok$2.80$0.10
Output $/Mtok$8.80$0.20
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 7B 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 33K tokens).

Which is cheaper, GLM 5.3 Prime or Qwen2.5 7B Instruct?

GLM 5.3 Prime costs $2.80 per million input tokens and $8.80 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 5.3 Prime or Qwen2.5 7B Instruct?

GLM 5.3 Prime, with 1M tokens against 33K.

Has any benchmark tested both GLM 5.3 Prime and Qwen2.5 7B Instruct?

No benchmark has tested both yet. GLM 5.3 Prime has none yet; Qwen2.5 7B Instruct on Epoch Capabilities Index, BALROG and GPQA Diamond (Epoch AI run). Compare them on price and context instead. Data as of October 22, 2026.

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