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Jamba 1.5 Large vs Ministral 3 (8B Reasoning 2512)

Ministral 3 (8B Reasoning 2512) leads the LLM Stats Score 16.5 to 0.9. Ministral 3 (8B Reasoning 2512) is 23.3x cheaper per token.

AI21 Labs · Mistral AI · Updated for 2026

Which is better?

Ministral 3 (8B Reasoning 2512) leads the overall LLM Stats Score 16.5 to 0.9, ranking #222 overall.

In the 1 individual benchmarks reported for both models, Ministral 3 (8B Reasoning 2512) wins 1; this is a narrower head-to-head signal than the composite indexes.

On price, Ministral 3 (8B Reasoning 2512) is roughly 23.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Ministral 3 (8B Reasoning 2512) also accepts a larger context window (262,100 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Jamba 1.5 Large

  • you want predictable pricing at $2.00/M input and $8.00/M output

Choose Ministral 3 (8B Reasoning 2512)

  • overall performance matters — it scores 16.5 and ranks #222 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • cost matters — it's about 23.3x cheaper per token
  • you process long inputs — it offers a 262,100 token context window
  • you want the most recent training data — it shipped Dec 2025

At a glance

The differences that matter most.

Core performance indexes
0.9
#322
16.5
#222
1.0
#314
16.6
#214
Cost, coverage & limits
Benchmark wins
0 of 1
1 of 1
Input price
$2.00 / M
$0.15 / M
Output price
$8.00 / M
$0.15 / M
Context window
256,000
262,100

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Jamba 1.5 Large
Ministral 3 (8B Reasoning 2512)
4.4#282
17.1#198
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

8 reported for Jamba 1.5 Large · 4 for Ministral 3 (8B Reasoning 2512)

1 shared

Jamba 1.5 Large outperforms in 0 benchmarks, while Ministral 3 (8B Reasoning 2512) is better at 1 benchmark (GPQA).

Ministral 3 (8B Reasoning 2512) significantly outperforms across most benchmarks.

Sun Sep 20 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Ministral 3 (8B Reasoning 2512) costs less

For input processing, Jamba 1.5 Large ($2.00/1M tokens) is 13.3x more expensive than Ministral 3 (8B Reasoning 2512) ($0.15/1M tokens).

For output processing, Jamba 1.5 Large ($8.00/1M tokens) is 53.3x more expensive than Ministral 3 (8B Reasoning 2512) ($0.15/1M tokens).

In conclusion, Jamba 1.5 Large is more expensive than Ministral 3 (8B Reasoning 2512).*

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

Lowest available price from all providers
Sun Sep 20 2026 • llm-stats.com
AI21 Labs
Jamba 1.5 Large
Input tokens$2.00
Output tokens$8.00
Best providerAWS Bedrock
Mistral AI
Ministral 3 (8B Reasoning 2512)
Input tokens$0.15
Output tokens$0.15
Best providerMistral
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

390.0B diff

Jamba 1.5 Large has 390.0B more parameters than Ministral 3 (8B Reasoning 2512), making it 4875.0% larger.

AI21 Labs
Jamba 1.5 Large
398.0Bparameters
Mistral AI
Ministral 3 (8B Reasoning 2512)
8.0Bparameters
398.0B
Jamba 1.5 Large
8.0B
Ministral 3 (8B Reasoning 2512)

Context Window

Maximum input and output token capacity

Ministral 3 (8B Reasoning 2512) accepts 262,100 input tokens compared to Jamba 1.5 Large's 256,000 tokens. Ministral 3 (8B Reasoning 2512) can generate longer responses up to 262,100 tokens, while Jamba 1.5 Large is limited to 256,000 tokens.

AI21 Labs
Jamba 1.5 Large
Input256,000 tokens
Output256,000 tokens
Mistral AI
Ministral 3 (8B Reasoning 2512)
Input262,100 tokens
Output262,100 tokens
Sun Sep 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Ministral 3 (8B Reasoning 2512) supports multimodal inputs, whereas Jamba 1.5 Large does not.

Ministral 3 (8B Reasoning 2512) can handle both text and other forms of data like images, making it suitable for multimodal applications.

Jamba 1.5 Large

Text
Images
Audio
Video

Ministral 3 (8B Reasoning 2512)

Text
Images
Audio
Video

License

Usage and distribution terms

Jamba 1.5 Large is licensed under Jamba Open Model License, while Ministral 3 (8B Reasoning 2512) uses Apache 2.0.

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

Jamba 1.5 Large

Jamba Open Model License

Open weights

Ministral 3 (8B Reasoning 2512)

Apache 2.0

Open weights

Release Timeline

When each model was launched

Jamba 1.5 Large was released on 2024-08-22, while Ministral 3 (8B Reasoning 2512) was released on 2025-12-04.

Ministral 3 (8B Reasoning 2512) is 16 months newer than Jamba 1.5 Large.

Jamba 1.5 Large

Aug 22, 2024

2.1 years ago

Ministral 3 (8B Reasoning 2512)

Dec 4, 2025

9 months ago

1.3yr newer

Knowledge Cutoff

When training data ends

Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while Ministral 3 (8B Reasoning 2512)'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 Ministral 3 (8B Reasoning 2512)'s cutoff date.

Jamba 1.5 Large

Mar 2024

Ministral 3 (8B Reasoning 2512)

Provider Availability

Jamba 1.5 Large is available from Bedrock, Google. Ministral 3 (8B Reasoning 2512) is available from Mistral AI.

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

Ministral 3 (8B Reasoning 2512)

mistral logo
Mistral
Input Price:Input: $0.15/1MOutput Price:Output: $0.15/1M
* 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 Jamba 1.5 Large and Ministral 3 (8B Reasoning 2512) side-by-side, then vote on the output you prefer.

Jamba 1.5 Large
✓ Preferred
Ministral 3 (8B Reasoning 2512)
Open in Playground

FAQ

Common questions about Jamba 1.5 Large vs Ministral 3 (8B Reasoning 2512).

Which is better, Jamba 1.5 Large or Ministral 3 (8B Reasoning 2512)?

Ministral 3 (8B Reasoning 2512) leads the LLM Stats Score 16.5 to 0.9. Jamba 1.5 Large is made by AI21 Labs and Ministral 3 (8B Reasoning 2512) is made by Mistral AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Jamba 1.5 Large compare to Ministral 3 (8B Reasoning 2512) in benchmarks?

Jamba 1.5 Large scores ARC-C: 93.0%, GSM8k: 87.0%, MMLU: 81.2%, Arena Hard: 65.4%, TruthfulQA: 58.3%. Ministral 3 (8B Reasoning 2512) scores AIME 2024: 86.0%, AIME 2025: 78.7%, GPQA: 66.8%, LiveCodeBench: 61.6%.

Is Jamba 1.5 Large cheaper than Ministral 3 (8B Reasoning 2512)?

Ministral 3 (8B Reasoning 2512) is 13.3x cheaper for input tokens. Jamba 1.5 Large costs $2.00/M input and $8.00/M output via bedrock. Ministral 3 (8B Reasoning 2512) costs $0.15/M input and $0.15/M output via mistral.

What are the context window sizes for Jamba 1.5 Large and Ministral 3 (8B Reasoning 2512)?

Jamba 1.5 Large supports 256K tokens and Ministral 3 (8B Reasoning 2512) supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Jamba 1.5 Large and Ministral 3 (8B Reasoning 2512)?

Key differences include LLM Stats Score (0.9 vs 16.5), context window (256K vs 262K), input pricing ($2.00 vs $0.15/M), multimodal support (no vs yes), licensing (Jamba Open Model License vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Jamba 1.5 Large and Ministral 3 (8B Reasoning 2512)?

Jamba 1.5 Large is developed by AI21 Labs and Ministral 3 (8B Reasoning 2512) is developed by Mistral AI.