Model Comparison

GLM-5 vs Llama 3.2 3B InstructWhich is better in 2026?

Comparing GLM-5 and Llama 3.2 3B Instruct across benchmarks, pricing, and capabilities.

Verdict: GLM-5 vs Llama 3.2 3B Instruct — which is better?

GLM-5 (by Zhipu AI) and Llama 3.2 3B Instruct (by Meta) 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.

On price, Llama 3.2 3B Instruct is roughly 124.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

GLM-5 also accepts a larger context window (200,000 input tokens), making it the stronger choice for long documents and large codebases.

Choose GLM-5 if…

  • you process long inputs — it offers a 200,000 token context window
  • you want the most recent training data — it shipped Feb 2026

Choose Llama 3.2 3B Instruct if…

  • cost matters — it's about 124.0x cheaper per token
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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5 and Llama 3.2 3B Instruct 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

Llama 3.2 3B Instruct costs less

For input processing, GLM-5 ($1.00/1M tokens) is 100.0x more expensive than Llama 3.2 3B Instruct ($0.01/1M tokens).

For output processing, GLM-5 ($3.20/1M tokens) is 160.0x more expensive than Llama 3.2 3B Instruct ($0.02/1M tokens).

In conclusion, GLM-5 is more expensive than Llama 3.2 3B Instruct.*

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

Lowest available price from all providers
Sat Jun 06 2026 • llm-stats.com
Zhipu AI
GLM-5
Input tokens$1.00
Output tokens$3.20
Best providerFriendliAI
Meta
Llama 3.2 3B Instruct
Input tokens$0.01
Output tokens$0.02
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

740.8B diff

GLM-5 has 740.8B more parameters than Llama 3.2 3B Instruct, making it 23077.6% larger.

Zhipu AI
GLM-5
744.0Bparameters
Meta
Llama 3.2 3B Instruct
3.2Bparameters
744.0B
GLM-5
3.2B
Llama 3.2 3B Instruct

Context Window

Maximum input and output token capacity

GLM-5 accepts 200,000 input tokens compared to Llama 3.2 3B Instruct's 128,000 tokens. Both models can generate responses up to 128,000 tokens.

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
Meta
Llama 3.2 3B Instruct
Input128,000 tokens
Output128,000 tokens
Sat Jun 06 2026 • llm-stats.com

License

Usage and distribution terms

GLM-5 is licensed under MIT, while Llama 3.2 3B Instruct uses Llama 3.2 Community License.

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

GLM-5

MIT

Open weights

Llama 3.2 3B Instruct

Llama 3.2 Community License

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Llama 3.2 3B Instruct was released on 2024-09-25.

GLM-5 is 17 months newer than Llama 3.2 3B Instruct.

GLM-5

Feb 11, 2026

3 months ago

1.4yr newer
Llama 3.2 3B Instruct

Sep 25, 2024

1.7 years ago

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-5 is available from FriendliAI, ZAI. Llama 3.2 3B Instruct is available from DeepInfra.

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

Llama 3.2 3B Instruct

deepinfra logo
Deepinfra
Input Price:Input: $0.01/1MOutput Price:Output: $0.02/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Zhipu AI
Larger context window (200,000 tokens)
Meta
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
Meta
Llama 3.2 3B Instruct

FAQ

Common questions about GLM-5 vs Llama 3.2 3B Instruct.

Which is better, GLM-5 or Llama 3.2 3B Instruct?

GLM-5 (Zhipu AI) and Llama 3.2 3B Instruct (Meta) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does GLM-5 compare to Llama 3.2 3B Instruct 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%. Llama 3.2 3B Instruct scores NIH/Multi-needle: 84.7%, ARC-C: 78.6%, GSM8k: 77.7%, IFEval: 77.4%, HellaSwag: 69.8%.

Is GLM-5 cheaper than Llama 3.2 3B Instruct?

Llama 3.2 3B Instruct is 100.0x cheaper for input tokens. GLM-5 costs $1.00/M input and $3.20/M output via friendli. Llama 3.2 3B Instruct costs $0.01/M input and $0.02/M output via deepinfra.

What are the context window sizes for GLM-5 and Llama 3.2 3B Instruct?

GLM-5 supports 200K tokens and Llama 3.2 3B Instruct supports 128K 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 Llama 3.2 3B Instruct?

Key differences include context window (200K vs 128K), input pricing ($1.00 vs $0.01/M), licensing (MIT vs Llama 3.2 Community License). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5 and Llama 3.2 3B Instruct?

GLM-5 is developed by Zhipu AI and Llama 3.2 3B Instruct is developed by Meta.

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