GLM 5.2 (batch) vs GLM 5.3 (batch)
| Signal | GLM 5.2 (batch) | Delta | GLM 5.3 (batch) |
|---|---|---|---|
Capabilities | 67 | -- | |
Benchmarks | 69 | -- | |
Pricing | 99 | -- | |
Context window size | 96 | -- | |
Recency | 100 | -- | |
Output Capacity | 96 | -- | |
| Overall Result | 0 wins | of 6 | 0 wins |
Score History
68.9
current score
Tied
right now
68.9
current score
GLM 5.2 (batch)
Zhipu AI
GLM 5.3 (batch)
Zhipu AI
| Metric | GLM 5.2 (batch) | GLM 5.3 (batch) | Winner |
|---|---|---|---|
| Overall Score | 69 | 69 | -- |
| Rank | #131 | #129 | GLM 5.3 (batch) |
| Quality Rank | #131 | #129 | GLM 5.3 (batch) |
| Adoption Rank | #131 | #129 | GLM 5.3 (batch) |
| Parameters | -- | -- | -- |
| Context Window | 1049K | 1049K | -- |
| Pricing | $0.37/$1.50/M | $0.37/$1.50/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 67 | GLM 5.2 (batch) |
| Benchmarks | 69 | 69 | GLM 5.2 (batch) |
| Pricing | 99 | 99 | GLM 5.2 (batch) |
| Context window size | 96 | 96 | GLM 5.2 (batch) |
| Recency | 100 | 100 | GLM 5.2 (batch) |
| Output Capacity | 96 | 96 | GLM 5.2 (batch) |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 69/100 (rank #131), placing it in the top 55% of all 290 models tracked.
Scores 69/100 (rank #129), placing it in the top 56% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Choose GLM 5.2 (batch) when you need:
- Step-by-step reasoning and chain-of-thought problem solving
- Self-hosted deployments where you need full control over the model
Choose GLM 5.3 (batch) when you need:
- Step-by-step reasoning and chain-of-thought problem solving
- Self-hosted deployments where you need full control over the model
Both models are priced similarly, so the decision comes down to quality and features rather than cost.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. GLM 5.2 (batch) also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (1049K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($1.50/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (69/100) correlates with better nuance, coherence, and style in long-form content
GLM 5.2 (batch) and GLM 5.3 (batch) are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
By Use Case
Best for Quality
GLM 5.2 (batch)
Marginally better benchmark scores; both are excellent
Best for Cost
GLM 5.2 (batch)
0% lower pricing; better value at scale
Best for Reliability
GLM 5.2 (batch)
Higher uptime and faster response speeds
Best for Prototyping
GLM 5.2 (batch)
Stronger community support and better developer experience
Best for Production
GLM 5.2 (batch)
Wider enterprise adoption and proven at scale
by Zhipu AI
- Choose for Quality - Marginally better benchmark scores; both are excellent
- Choose for Cost - 0% lower pricing; better value at scale
- Choose for Reliability - Higher uptime and faster response speeds
- Choose for Prototyping - Stronger community support and better developer experience
- Choose for Production - Wider enterprise adoption and proven at scale
| Capability | GLM 5.2 (batch) | GLM 5.3 (batch) |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
GLM 5.2 (batch)
Zhipu AI
GLM 5.3 (batch)
Zhipu AI
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | GLM 5.2 (batch) | GLM 5.3 (batch) |
|---|---|---|
| Context Window | 1.0M | 1.0M |
| Max Output Tokens | 943,718 | 943,718 |
| Open Source | Yes | Yes |
| Created | Jun 16, 2026 | Aug 18, 2026 |