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GLM-5.3 vs Granite 3.3 8B Instruct

Comparing GLM-5.3 and Granite 3.3 8B Instruct across benchmarks, pricing, and capabilities.

Zhipu AI · IBM · Updated for 2026

Which is better?

GLM-5.3 and Granite 3.3 8B Instruct trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

On price, Granite 3.3 8B Instruct is roughly 4.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

GLM-5.3 also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current benchmark, pricing, and model metadata for 2026.

Choose GLM-5.3

  • you process long inputs — it offers a 1,000,000 token context window
  • you want the most recent training data — it shipped Aug 2026

Choose Granite 3.3 8B Instruct

  • cost matters — it's about 4.3x cheaper per token
  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Benchmark wins
Input price
$1.40 / M
$0.50 / M
Output price
$4.40 / M
$0.50 / M
Context window
1,000,000
128,000
Released
Aug 2026
Apr 2025
License
Unknown
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5.3 and Granite 3.3 8B Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Pricing Analysis

Price comparison per million tokens

Granite 3.3 8B Instruct costs less

For input processing, GLM-5.3 ($1.40/1M tokens) is 2.8x more expensive than Granite 3.3 8B Instruct ($0.50/1M tokens).

For output processing, GLM-5.3 ($4.40/1M tokens) is 8.8x more expensive than Granite 3.3 8B Instruct ($0.50/1M tokens).

In conclusion, GLM-5.3 is more expensive than Granite 3.3 8B Instruct.*

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

Lowest available price from all providers
Tue Aug 25 2026 • llm-stats.com
Zhipu AI
GLM-5.3
Input tokens$1.40
Output tokens$4.40
Best providerUnknown Organization
IBM
Granite 3.3 8B Instruct
Input tokens$0.50
Output tokens$0.50
Best providerReplicate
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

745.0B diff

GLM-5.3 has 745.0B more parameters than Granite 3.3 8B Instruct, making it 9312.5% larger.

Zhipu AI
GLM-5.3
753.0Bparameters
IBM
Granite 3.3 8B Instruct
8.0Bparameters
753.0B
GLM-5.3
8.0B
Granite 3.3 8B Instruct

Context Window

Maximum input and output token capacity

GLM-5.3 accepts 1,000,000 input tokens compared to Granite 3.3 8B Instruct's 128,000 tokens. GLM-5.3 can generate longer responses up to 128,000 tokens, while Granite 3.3 8B Instruct is limited to 8,192 tokens.

Zhipu AI
GLM-5.3
Input1,000,000 tokens
Output128,000 tokens
IBM
Granite 3.3 8B Instruct
Input128,000 tokens
Output8,192 tokens
Tue Aug 25 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Granite 3.3 8B Instruct supports multimodal inputs, whereas GLM-5.3 does not.

Granite 3.3 8B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5.3

Text
Images
Audio
Video

Granite 3.3 8B Instruct

Text
Images
Audio
Video

Release Timeline

When each model was launched

GLM-5.3 was released on 2026-08-14, while Granite 3.3 8B Instruct was released on 2025-04-16.

GLM-5.3 is 16 months newer than Granite 3.3 8B Instruct.

GLM-5.3

Aug 14, 2026

1 weeks ago

1.3yr newer
Granite 3.3 8B Instruct

Apr 16, 2025

1.4 years ago

Knowledge Cutoff

When training data ends

Granite 3.3 8B Instruct has a documented knowledge cutoff of 2024-04-01, while GLM-5.3's cutoff date is not specified.

We can confirm Granite 3.3 8B Instruct's training data extends to 2024-04-01, but cannot make a direct comparison without GLM-5.3's cutoff date.

GLM-5.3

Granite 3.3 8B Instruct

Apr 2024

Provider Availability

GLM-5.3 is available from ZAI. Granite 3.3 8B Instruct is available from Replicate.

GLM-5.3

z logo
Unknown Organization
Input Price:Input: $1.40/1MOutput Price:Output: $4.40/1M

Granite 3.3 8B Instruct

replicate logo
Replicate
Input Price:Input: $0.50/1MOutput Price:Output: $0.50/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against GLM-5.3 and Granite 3.3 8B Instruct side-by-side, then vote on the output you prefer.

GLM-5.3
✓ Preferred
Granite 3.3 8B Instruct
Open in Playground

FAQ

Common questions about GLM-5.3 vs Granite 3.3 8B Instruct.

Which is better, GLM-5.3 or Granite 3.3 8B Instruct?

GLM-5.3 (Zhipu AI) and Granite 3.3 8B Instruct (IBM) 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-5.3 compare to Granite 3.3 8B Instruct in benchmarks?

GLM-5.3 scores Terminal-Bench 2.1: 88.2%, CyberGym: 84.5%, FrontierSWE: 78.1%, Toolathlon: 73.0%, DeepSWE 1.1: 66.9%. Granite 3.3 8B Instruct scores HumanEval: 89.7%, AttaQ: 88.5%, HumanEval+: 86.1%, AIME 2024: 81.2%, GSM8k: 80.9%.

Is GLM-5.3 cheaper than Granite 3.3 8B Instruct?

Granite 3.3 8B Instruct is 2.8x cheaper for input tokens. GLM-5.3 costs $1.40/M input and $4.40/M output via z. Granite 3.3 8B Instruct costs $0.50/M input and $0.50/M output via replicate.

What are the context window sizes for GLM-5.3 and Granite 3.3 8B Instruct?

GLM-5.3 supports 1.0M tokens and Granite 3.3 8B 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-5.3 and Granite 3.3 8B Instruct?

Key differences include context window (1.0M vs 128K), input pricing ($1.40 vs $0.50/M), multimodal support (no vs yes), licensing (Unknown vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3 and Granite 3.3 8B Instruct?

GLM-5.3 is developed by Zhipu AI and Granite 3.3 8B Instruct is developed by IBM.