Comparison · AI Models & LLMs
GLM-4.6 vs GLM-5
The same method as our rankings: scores from the engine, sourced data, verified prices.
Details: GLM-4.6 →Details: GLM-5 →

GLM-4.6
Z.ai
Full sheet →74.6/100
- SWE-bench accuracy
- 68.2%
- Context window
- 200k tokens
- Maximum output
- 128k tokens
- Input cost per 1M tokens
- $0.60
GLM-5
Z.ai
Full sheet →71.3/100
- SWE-bench accuracy
- 72.8%
- Context window
- 200k tokens
- Maximum output
- 128k tokens
- Input cost per 1M tokens
- $1.00
The verdicttake the GLM-4.6, it leads in 2 of 4 areas — above all on Input cost per 1M tokens.
The other one stays ahead on SWE-bench Verified (resolved %).
- GLM-4.6 better for: Input cost per 1M tokens, Output cost per 1M tokens
- GLM-5 better for: SWE-bench Verified (resolved %)
Basis: 6 measured differences across 9 compared characteristics. The scores come from the ranking engine.
The numbers side by side
Each bar is the product's verified figure; on every row the longer bar is the higher of the two values.
- SWE-bench accuracyhigher is better
- Context windowhigher is better
- Maximum outputhigher is better
- Input cost per 1M tokenslower is better
- Cached input cost per 1M tokenslower is better
- Output cost per 1M tokenslower is better
Who wins where
- For codingGLM-5SWE-bench accuracy
- Cheaper to readGLM-4.6Input cost per 1M tokens
- Cheaper to writeGLM-4.6Output cost per 1M tokens
Data sheet side by side
Performance
Memory and answer length
API prices
Size and openness
What a month costs
Change the volumes: the cost is worked out from the verified list prices above.
The scenarios are only starting points to edit, not data about your use. List prices without discounts, time-of-day rates or taxes.
Questions and answers
For coding: GLM-4.6 or GLM-5?
GLM-5: 72.8% against 68.2% (SWE-bench accuracy).
Cheaper to read: GLM-4.6 or GLM-5?
GLM-4.6: $0.60 against $1.00 (Input cost per 1M tokens).
Cheaper to write: GLM-4.6 or GLM-5?
GLM-4.6: $2.20 against $3.20 (Output cost per 1M tokens).
Area by area
The same yardstick for both: the engine's score out of 100 on every criterion of the profile.
- SWE-bench Verified (resolved %)
- Context window
- Output cost per 1M tokens
- Input cost per 1M tokens
