Model Comparison

GLM-5 vs Gemini 3 FlashWhich is better in 2026?

Both models are evenly matched across the benchmarks. Gemini 3 Flash is 1.4x cheaper per token.

Verdict: GLM-5 vs Gemini 3 Flash — which is better?

GLM-5 (by Zhipu AI) and Gemini 3 Flash (by Google) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

GLM-5 outperforms in 2 benchmarks (MCP Atlas, Terminal-Bench 2.0), while Gemini 3 Flash is better at 2 benchmarks (SWE-Bench Verified, t2-bench). Both models are evenly matched across the benchmarks.

On price, Gemini 3 Flash is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Gemini 3 Flash also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.

Choose GLM-5 if…

  • you want the most recent training data — it shipped Feb 2026
  • you need open weights you can self-host or fine-tune

Choose Gemini 3 Flash if…

  • cost matters — it's about 1.4x cheaper per token
  • you process long inputs — it offers a 1,000,000 token context window

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

GLM-5 outperforms in 2 benchmarks (MCP Atlas, Terminal-Bench 2.0), while Gemini 3 Flash is better at 2 benchmarks (SWE-Bench Verified, t2-bench).

Both models are evenly matched across the benchmarks.

Tue Jun 16 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Gemini 3 Flash costs less

For input processing, GLM-5 ($1.00/1M tokens) is 2.0x more expensive than Gemini 3 Flash ($0.50/1M tokens).

For output processing, GLM-5 ($3.20/1M tokens) is 1.1x more expensive than Gemini 3 Flash ($3.00/1M tokens).

In conclusion, GLM-5 is more expensive than Gemini 3 Flash.*

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

Lowest available price from all providers
Tue Jun 16 2026 • llm-stats.com
Zhipu AI
GLM-5
Input tokens$1.00
Output tokens$3.20
Best providerFriendliAI
Google
Gemini 3 Flash
Input tokens$0.50
Output tokens$3.00
Best providerGoogle
Notice missing or incorrect data?Start an Issue

Context Window

Maximum input and output token capacity

Gemini 3 Flash accepts 1,000,000 input tokens compared to GLM-5's 200,000 tokens. GLM-5 can generate longer responses up to 128,000 tokens, while Gemini 3 Flash is limited to 65,536 tokens.

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
Google
Gemini 3 Flash
Input1,000,000 tokens
Output65,536 tokens
Tue Jun 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 3 Flash supports multimodal inputs, whereas GLM-5 does not.

Gemini 3 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5

Text
Images
Audio
Video

Gemini 3 Flash

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5 is licensed under MIT, while Gemini 3 Flash uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

GLM-5

MIT

Open weights

Gemini 3 Flash

Proprietary

Closed source

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Gemini 3 Flash was released on 2025-12-17.

GLM-5 is 2 months newer than Gemini 3 Flash.

GLM-5

Feb 11, 2026

4 months ago

1mo newer
Gemini 3 Flash

Dec 17, 2025

6 months ago

Knowledge Cutoff

When training data ends

Gemini 3 Flash has a documented knowledge cutoff of 2025-01-31, while GLM-5's cutoff date is not specified.

We can confirm Gemini 3 Flash's training data extends to 2025-01-31, but cannot make a direct comparison without GLM-5's cutoff date.

GLM-5

Gemini 3 Flash

Jan 2025

Provider Availability

GLM-5 is available from FriendliAI, ZAI. Gemini 3 Flash is available from Google.

GLM-5

friendli logo
FriendliAI
Input Price:Input: $1.00/1MOutput Price:Output: $3.20/1M
z logo
Unknown Organization
Input Price:Input: $1.00/1MOutput Price:Output: $3.20/1M

Gemini 3 Flash

google logo
Google
Input Price:Input: $0.50/1MOutput Price:Output: $3.00/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Has open weights
Higher MCP Atlas score (67.8% vs 57.4%)
Higher Terminal-Bench 2.0 score (56.2% vs 47.6%)
Larger context window (1,000,000 tokens)
Supports multimodal inputs
Less expensive input tokens
Less expensive output tokens
Higher SWE-Bench Verified score (78.0% vs 77.8%)
Higher t2-bench score (90.2% vs 89.7%)

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
Google
Gemini 3 Flash

FAQ

Common questions about GLM-5 vs Gemini 3 Flash.

Which is better, GLM-5 or Gemini 3 Flash?

Both models are evenly matched across the benchmarks. GLM-5 is made by Zhipu AI and Gemini 3 Flash is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GLM-5 compare to Gemini 3 Flash in benchmarks?

GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. Gemini 3 Flash scores Vending-Bench 2: 100.0%, AIME 2025: 99.7%, Global PIQA: 92.8%, MMMLU: 91.8%, GPQA: 90.4%.

Is GLM-5 cheaper than Gemini 3 Flash?

Gemini 3 Flash is 2.0x cheaper for input tokens. GLM-5 costs $1.00/M input and $3.20/M output via friendli. Gemini 3 Flash costs $0.50/M input and $3.00/M output via google.

What are the context window sizes for GLM-5 and Gemini 3 Flash?

GLM-5 supports 200K tokens and Gemini 3 Flash supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-5 and Gemini 3 Flash?

Key differences include context window (200K vs 1.0M), input pricing ($1.00 vs $0.50/M), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5 and Gemini 3 Flash?

GLM-5 is developed by Zhipu AI and Gemini 3 Flash is developed by Google.