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Jamba 1.5 Large vs Qwen3 VL 4B Thinking

Qwen3 VL 4B Thinking leads the LLM Stats Score 12.9 to 0.9. Qwen3 VL 4B Thinking is 10.8x cheaper per token.

AI21 Labs · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Qwen3 VL 4B Thinking leads the overall LLM Stats Score 12.9 to 0.9, ranking #248 overall.

In the 3 individual benchmarks reported for both models, Qwen3 VL 4B Thinking wins 3; this is a narrower head-to-head signal than the composite indexes.

On price, Qwen3 VL 4B Thinking is roughly 10.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Qwen3 VL 4B Thinking also accepts a larger context window (262,144 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 Qwen3 VL 4B Thinking

  • overall performance matters — it scores 12.9 and ranks #248 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 3 of 3 exact shared results
  • cost matters — it's about 10.8x cheaper per token
  • you process long inputs — it offers a 262,144 token context window
  • you want the most recent training data — it shipped Sep 2025

At a glance

The differences that matter most.

Core performance indexes
0.9
#321
12.9
#248
1.0
#313
14.0
#230
Cost, coverage & limits
Benchmark wins
0 of 3
3 of 3
Input price
$2.00 / M
$0.10 / M
Output price
$8.00 / M
$1.00 / M
Context window
256,000
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
Jamba 1.5 Large
Qwen3 VL 4B Thinking
4.4#282
15.6#216
5.3#169
16.5#112
5.3#155
15.9#98
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

8 reported for Jamba 1.5 Large · 48 for Qwen3 VL 4B Thinking

3 shared

Jamba 1.5 Large outperforms in 0 benchmarks, while Qwen3 VL 4B Thinking is better at 3 benchmarks (GPQA, MMLU, MMLU-Pro).

Qwen3 VL 4B Thinking significantly outperforms across most benchmarks.

Sat Sep 12 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Qwen3 VL 4B Thinking costs less

For input processing, Jamba 1.5 Large ($2.00/1M tokens) is 20.0x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).

For output processing, Jamba 1.5 Large ($8.00/1M tokens) is 8.0x more expensive than Qwen3 VL 4B Thinking ($1.00/1M tokens).

In conclusion, Jamba 1.5 Large is more expensive than Qwen3 VL 4B Thinking.*

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

Lowest available price from all providers
Sat Sep 12 2026 • llm-stats.com
AI21 Labs
Jamba 1.5 Large
Input tokens$2.00
Output tokens$8.00
Best providerAWS Bedrock
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
Input tokens$0.10
Output tokens$1.00
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

394.0B diff

Jamba 1.5 Large has 394.0B more parameters than Qwen3 VL 4B Thinking, making it 9850.0% larger.

AI21 Labs
Jamba 1.5 Large
398.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
4.0Bparameters
398.0B
Jamba 1.5 Large
4.0B
Qwen3 VL 4B Thinking

Context Window

Maximum input and output token capacity

Qwen3 VL 4B Thinking accepts 262,144 input tokens compared to Jamba 1.5 Large's 256,000 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while Jamba 1.5 Large is limited to 256,000 tokens.

AI21 Labs
Jamba 1.5 Large
Input256,000 tokens
Output256,000 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
Input262,144 tokens
Output262,144 tokens
Sat Sep 12 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Qwen3 VL 4B Thinking supports multimodal inputs, whereas Jamba 1.5 Large does not.

Qwen3 VL 4B Thinking 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

Qwen3 VL 4B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Jamba 1.5 Large is licensed under Jamba Open Model License, while Qwen3 VL 4B Thinking 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

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Jamba 1.5 Large was released on 2024-08-22, while Qwen3 VL 4B Thinking was released on 2025-09-22.

Qwen3 VL 4B Thinking is 13 months newer than Jamba 1.5 Large.

Jamba 1.5 Large

Aug 22, 2024

2.1 years ago

Qwen3 VL 4B Thinking

Sep 22, 2025

11 months ago

1.1yr newer

Knowledge Cutoff

When training data ends

Jamba 1.5 Large has a documented knowledge cutoff of 2024-03-05, while Qwen3 VL 4B Thinking'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 Qwen3 VL 4B Thinking's cutoff date.

Jamba 1.5 Large

Mar 2024

Qwen3 VL 4B Thinking

Provider Availability

Jamba 1.5 Large is available from Bedrock, Google. Qwen3 VL 4B Thinking is available from DeepInfra.

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

Qwen3 VL 4B Thinking

deepinfra logo
Deepinfra
Input Price:Input: $0.10/1MOutput Price:Output: $1.00/1M
* 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 Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.

Jamba 1.5 Large
✓ Preferred
Qwen3 VL 4B Thinking
Open in Playground

FAQ

Common questions about Jamba 1.5 Large vs Qwen3 VL 4B Thinking.

Which is better, Jamba 1.5 Large or Qwen3 VL 4B Thinking?

Qwen3 VL 4B Thinking leads the LLM Stats Score 12.9 to 0.9. Jamba 1.5 Large is made by AI21 Labs and Qwen3 VL 4B Thinking is made by Alibaba Cloud / Qwen Team. 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 Qwen3 VL 4B Thinking in benchmarks?

Jamba 1.5 Large scores ARC-C: 93.0%, GSM8k: 87.0%, MMLU: 81.2%, Arena Hard: 65.4%, TruthfulQA: 58.3%. Qwen3 VL 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.

Is Jamba 1.5 Large cheaper than Qwen3 VL 4B Thinking?

Qwen3 VL 4B Thinking is 20.0x cheaper for input tokens. Jamba 1.5 Large costs $2.00/M input and $8.00/M output via bedrock. Qwen3 VL 4B Thinking costs $0.10/M input and $1.00/M output via deepinfra.

What are the context window sizes for Jamba 1.5 Large and Qwen3 VL 4B Thinking?

Jamba 1.5 Large supports 256K tokens and Qwen3 VL 4B Thinking 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 Qwen3 VL 4B Thinking?

Key differences include LLM Stats Score (0.9 vs 12.9), context window (256K vs 262K), input pricing ($2.00 vs $0.10/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 Qwen3 VL 4B Thinking?

Jamba 1.5 Large is developed by AI21 Labs and Qwen3 VL 4B Thinking is developed by Alibaba Cloud / Qwen Team.