Model Comparison

Codestral-22B vs Jamba 1.5 Large

Comparing Codestral-22B and Jamba 1.5 Large across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Codestral-22B and Jamba 1.5 Large don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

375.8B diff

Jamba 1.5 Large has 375.8B more parameters than Codestral-22B, making it 1692.8% larger.

Mistral AI
Codestral-22B
22.2Bparameters
AI21 Labs
Jamba 1.5 Large
398.0Bparameters
22.2B
Codestral-22B
398.0B
Jamba 1.5 Large

Context Window

Maximum input and output token capacity

Only Jamba 1.5 Large specifies input context (256,000 tokens). Only Jamba 1.5 Large specifies output context (256,000 tokens).

Mistral AI
Codestral-22B
Input- tokens
Output- tokens
AI21 Labs
Jamba 1.5 Large
Input256,000 tokens
Output256,000 tokens
Tue Jun 02 2026 • llm-stats.com

License

Usage and distribution terms

Codestral-22B is licensed under MNPL-0.1, 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.

Codestral-22B

MNPL-0.1

Open weights

Jamba 1.5 Large

Jamba Open Model License

Open weights

Release Timeline

When each model was launched

Codestral-22B was released on 2024-05-29, while Jamba 1.5 Large was released on 2024-08-22.

Jamba 1.5 Large is 3 months newer than Codestral-22B.

Codestral-22B

May 29, 2024

2.0 years ago

Jamba 1.5 Large

Aug 22, 2024

1.8 years ago

2mo newer

Knowledge Cutoff

When training data ends

Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while Codestral-22B'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 Codestral-22B's cutoff date.

Codestral-22B

—

Jamba 1.5 Large

Mar 2024

Outputs Comparison

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Key Takeaways

Mistral AI

No standout differentiators in the data we have for this pair.

AI21 Labs
Larger context window (256,000 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
Mistral AI
Codestral-22B
AI21 Labs
Jamba 1.5 Large

FAQ

Common questions about Codestral-22B vs Jamba 1.5 Large.

Which is better, Codestral-22B or Jamba 1.5 Large?

Codestral-22B (Mistral AI) and Jamba 1.5 Large (AI21 Labs) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Codestral-22B compare to Jamba 1.5 Large in benchmarks?

Codestral-22B scores HumanEvalFIM-Average: 91.6%, HumanEval: 81.1%, MBPP: 78.2%, Spider: 63.5%, HumanEval-Average: 61.5%. Jamba 1.5 Large scores ARC-C: 93.0%, GSM8k: 87.0%, MMLU: 81.2%, Arena Hard: 65.4%, TruthfulQA: 58.3%.

What are the context window sizes for Codestral-22B and Jamba 1.5 Large?

Codestral-22B supports an unknown number of 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 Codestral-22B and Jamba 1.5 Large?

Key differences include licensing (MNPL-0.1 vs Jamba Open Model License). See the full comparison above for benchmark-by-benchmark results.

Who makes Codestral-22B and Jamba 1.5 Large?

Codestral-22B is developed by Mistral AI and Jamba 1.5 Large is developed by AI21 Labs.