Model Comparison

GLM-4.7 vs Jamba 1.5 Large

GLM-4.7 significantly outperforms across most benchmarks. GLM-4.7 is 3.5x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

GLM-4.7 outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Jamba 1.5 Large is better at 0 benchmarks.

GLM-4.7 significantly outperforms across most benchmarks.

Thu May 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

GLM-4.7 costs less

For input processing, GLM-4.7 ($0.60/1M tokens) is 3.3x cheaper than Jamba 1.5 Large ($2.00/1M tokens).

For output processing, GLM-4.7 ($2.20/1M tokens) is 3.6x cheaper than Jamba 1.5 Large ($8.00/1M tokens).

In conclusion, Jamba 1.5 Large is more expensive than GLM-4.7.*

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

Lowest available price from all providers
Thu May 14 2026 • llm-stats.com
Zhipu AI
GLM-4.7
Input tokens$0.60
Output tokens$2.20
Best providerFireworks
AI21 Labs
Jamba 1.5 Large
Input tokens$2.00
Output tokens$8.00
Best providerAWS Bedrock
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

40.0B diff

Jamba 1.5 Large has 40.0B more parameters than GLM-4.7, making it 11.2% larger.

Zhipu AI
GLM-4.7
358.0Bparameters
AI21 Labs
Jamba 1.5 Large
398.0Bparameters
358.0B
GLM-4.7
398.0B
Jamba 1.5 Large

Context Window

Maximum input and output token capacity

Jamba 1.5 Large accepts 256,000 input tokens compared to GLM-4.7's 202,800 tokens. Jamba 1.5 Large can generate longer responses up to 256,000 tokens, while GLM-4.7 is limited to 131,072 tokens.

Zhipu AI
GLM-4.7
Input202,800 tokens
Output131,072 tokens
AI21 Labs
Jamba 1.5 Large
Input256,000 tokens
Output256,000 tokens
Thu May 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-4.7 supports multimodal inputs, whereas Jamba 1.5 Large does not.

GLM-4.7 can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-4.7

Text
Images
Audio
Video

Jamba 1.5 Large

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-4.7 is licensed under MIT, while Jamba 1.5 Large uses Jamba Open Model License.

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

GLM-4.7

MIT

Open weights

Jamba 1.5 Large

Jamba Open Model License

Open weights

Release Timeline

When each model was launched

GLM-4.7 was released on 2025-12-22, while Jamba 1.5 Large was released on 2024-08-22.

GLM-4.7 is 16 months newer than Jamba 1.5 Large.

GLM-4.7

Dec 22, 2025

4 months ago

1.3yr newer
Jamba 1.5 Large

Aug 22, 2024

1.7 years ago

Knowledge Cutoff

When training data ends

Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while GLM-4.7's cutoff date is not specified.

We can confirm Jamba 1.5 Large's training data extends to 2024-03-05, but cannot make a direct comparison without GLM-4.7's cutoff date.

GLM-4.7

—

Jamba 1.5 Large

Mar 2024

Provider Availability

GLM-4.7 is available from Fireworks, Novita. Jamba 1.5 Large is available from Bedrock, Google.

GLM-4.7

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

Jamba 1.5 Large

bedrock logo
AWS Bedrock
Input Price:Input: $2.00/1MOutput Price:Output: $8.00/1M
google logo
Google
Input Price:Input: $2.00/1MOutput Price:Output: $8.00/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Zhipu AI
Supports multimodal inputs
Less expensive input tokens
Less expensive output tokens
Higher GPQA score (85.7% vs 36.9%)
Higher MMLU-Pro score (84.3% vs 53.5%)
AI21 Labs
Larger context window (256,000 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.7
AI21 Labs
Jamba 1.5 Large

FAQ

Common questions about GLM-4.7 vs Jamba 1.5 Large.

Which is better, GLM-4.7 or Jamba 1.5 Large?

GLM-4.7 significantly outperforms across most benchmarks. GLM-4.7 is made by Zhipu AI and Jamba 1.5 Large is made by AI21 Labs. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GLM-4.7 compare to Jamba 1.5 Large in benchmarks?

GLM-4.7 scores AIME 2025: 95.7%, Tau-bench: 87.4%, GPQA: 85.7%, LiveCodeBench v6: 84.9%, MMLU-Pro: 84.3%. Jamba 1.5 Large scores ARC-C: 93.0%, GSM8k: 87.0%, MMLU: 81.2%, Arena Hard: 65.4%, TruthfulQA: 58.3%.

Is GLM-4.7 cheaper than Jamba 1.5 Large?

GLM-4.7 is 3.3x cheaper for input tokens. GLM-4.7 costs $0.60/M input and $2.20/M output via fireworks. Jamba 1.5 Large costs $2.00/M input and $8.00/M output via bedrock.

What are the context window sizes for GLM-4.7 and Jamba 1.5 Large?

GLM-4.7 supports 203K tokens and Jamba 1.5 Large supports 256K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-4.7 and Jamba 1.5 Large?

Key differences include context window (203K vs 256K), input pricing ($0.60 vs $2.00/M), multimodal support (yes vs no), licensing (MIT vs Jamba Open Model License). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.7 and Jamba 1.5 Large?

GLM-4.7 is developed by Zhipu AI and Jamba 1.5 Large is developed by AI21 Labs.