Model Comparison

GLM-5 vs Phi-3.5-mini-instructWhich is better in 2026?

Comparing GLM-5 and Phi-3.5-mini-instruct across benchmarks, pricing, and capabilities.

Verdict: GLM-5 vs Phi-3.5-mini-instruct — which is better?

GLM-5 (by Zhipu AI) and Phi-3.5-mini-instruct (by Microsoft) 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, Phi-3.5-mini-instruct is roughly 15.5x 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 Phi-3.5-mini-instruct if…

  • cost matters — it's about 15.5x cheaper per token
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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5 and Phi-3.5-mini-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

Phi-3.5-mini-instruct costs less

For input processing, GLM-5 ($1.00/1M tokens) is 10.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

For output processing, GLM-5 ($3.20/1M tokens) is 32.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

In conclusion, GLM-5 is more expensive than Phi-3.5-mini-instruct.*

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

Lowest available price from all providers
Sun Jun 07 2026 • llm-stats.com
Zhipu AI
GLM-5
Input tokens$1.00
Output tokens$3.20
Best providerFriendliAI
Microsoft
Phi-3.5-mini-instruct
Input tokens$0.10
Output tokens$0.10
Best providerAzure
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

740.2B diff

GLM-5 has 740.2B more parameters than Phi-3.5-mini-instruct, making it 19478.9% larger.

Zhipu AI
GLM-5
744.0Bparameters
Microsoft
Phi-3.5-mini-instruct
3.8Bparameters
744.0B
GLM-5
3.8B
Phi-3.5-mini-instruct

Context Window

Maximum input and output token capacity

GLM-5 accepts 200,000 input tokens compared to Phi-3.5-mini-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
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Sun Jun 07 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

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

GLM-5

MIT

Open weights

Phi-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Phi-3.5-mini-instruct was released on 2024-08-23.

GLM-5 is 18 months newer than Phi-3.5-mini-instruct.

GLM-5

Feb 11, 2026

3 months ago

1.5yr newer
Phi-3.5-mini-instruct

Aug 23, 2024

1.8 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. Phi-3.5-mini-instruct is available from Azure.

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

Phi-3.5-mini-instruct

azure logo
Azure
Input Price:Input: $0.10/1MOutput Price:Output: $0.10/1M
* 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)
Microsoft
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
Microsoft
Phi-3.5-mini-instruct

FAQ

Common questions about GLM-5 vs Phi-3.5-mini-instruct.

Which is better, GLM-5 or Phi-3.5-mini-instruct?

GLM-5 (Zhipu AI) and Phi-3.5-mini-instruct (Microsoft) 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 Phi-3.5-mini-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%. Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.

Is GLM-5 cheaper than Phi-3.5-mini-instruct?

Phi-3.5-mini-instruct is 10.0x cheaper for input tokens. GLM-5 costs $1.00/M input and $3.20/M output via friendli. Phi-3.5-mini-instruct costs $0.10/M input and $0.10/M output via azure.

What are the context window sizes for GLM-5 and Phi-3.5-mini-instruct?

GLM-5 supports 200K tokens and Phi-3.5-mini-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 Phi-3.5-mini-instruct?

Key differences include context window (200K vs 128K), input pricing ($1.00 vs $0.10/M). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5 and Phi-3.5-mini-instruct?

GLM-5 is developed by Zhipu AI and Phi-3.5-mini-instruct is developed by Microsoft.

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