Model Comparison

GLM-4.5V vs GLM-4.7-Flash

Comparing GLM-4.5V and GLM-4.7-Flash across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-4.5V and GLM-4.7-Flash don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

GLM-4.7-Flash costs less

For input processing, GLM-4.5V ($0.55/1M tokens) is 7.9x more expensive than GLM-4.7-Flash ($0.07/1M tokens).

For output processing, GLM-4.5V ($2.19/1M tokens) is 5.5x more expensive than GLM-4.7-Flash ($0.40/1M tokens).

In conclusion, GLM-4.5V is more expensive than GLM-4.7-Flash.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Wed Apr 29 2026 • llm-stats.com
Zhipu AI
GLM-4.5V
Input tokens$0.55
Output tokens$2.19
Best providerFireworks
Zhipu AI
GLM-4.7-Flash
Input tokens$0.07
Output tokens$0.40
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

78.0B diff

GLM-4.5V has 78.0B more parameters than GLM-4.7-Flash, making it 260.0% larger.

Zhipu AI
GLM-4.5V
108.0Bparameters
Zhipu AI
GLM-4.7-Flash
30.0Bparameters
108.0B
GLM-4.5V
30.0B
GLM-4.7-Flash

Context Window

Maximum input and output token capacity

GLM-4.5V accepts 131,072 input tokens compared to GLM-4.7-Flash's 128,000 tokens. GLM-4.5V can generate longer responses up to 131,072 tokens, while GLM-4.7-Flash is limited to 16,384 tokens.

Zhipu AI
GLM-4.5V
Input131,072 tokens
Output131,072 tokens
Zhipu AI
GLM-4.7-Flash
Input128,000 tokens
Output16,384 tokens
Wed Apr 29 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-4.5V supports multimodal inputs, whereas GLM-4.7-Flash does not.

GLM-4.5V can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-4.5V

Text
Images
Audio
Video

GLM-4.7-Flash

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

GLM-4.5V

MIT

Open weights

GLM-4.7-Flash

MIT

Open weights

Release Timeline

When each model was launched

GLM-4.5V was released on 2025-08-11, while GLM-4.7-Flash was released on 2026-01-19.

GLM-4.7-Flash is 5 months newer than GLM-4.5V.

GLM-4.5V

Aug 11, 2025

8 months ago

GLM-4.7-Flash

Jan 19, 2026

3 months ago

5mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

GLM-4.5V is available from Fireworks, Novita. GLM-4.7-Flash is available from ZAI.

GLM-4.5V

fireworks logo
Fireworks
Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M
novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M

GLM-4.7-Flash

z logo
Unknown Organization
Input Price:Input: $0.07/1MOutput Price:Output: $0.40/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Zhipu AI
Larger context window (131,072 tokens)
Supports multimodal inputs
Zhipu AI
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.5V
Zhipu AI
GLM-4.7-Flash

FAQ

Common questions about GLM-4.5V vs GLM-4.7-Flash

GLM-4.5V (Zhipu AI) and GLM-4.7-Flash (Zhipu AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
GLM-4.7-Flash scores AIME 2025: 91.6%, Tau-bench: 79.5%, GPQA: 75.2%, SWE-Bench Verified: 59.2%, BrowseComp: 42.8%.
GLM-4.7-Flash is 7.9x cheaper for input tokens. GLM-4.5V costs $0.55/M input and $2.19/M output via fireworks. GLM-4.7-Flash costs $0.07/M input and $0.40/M output via z.
GLM-4.5V supports 131K tokens and GLM-4.7-Flash supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (131K vs 128K), input pricing ($0.55 vs $0.07/M), multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.