Model Comparison

Mistral NeMo Instruct vs Qwen3 VL 4B Thinking

Qwen3 VL 4B Thinking significantly outperforms across most benchmarks. Mistral NeMo Instruct is 2.2x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

Mistral NeMo Instruct outperforms in 0 benchmarks, while Qwen3 VL 4B Thinking is better at 1 benchmark (MMLU).

Qwen3 VL 4B Thinking significantly outperforms across most benchmarks.

Fri May 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Mistral NeMo Instruct costs less

For input processing, Mistral NeMo Instruct ($0.15/1M tokens) is 1.5x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).

For output processing, Mistral NeMo Instruct ($0.15/1M tokens) is 6.7x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).

In conclusion, Qwen3 VL 4B Thinking is more expensive than Mistral NeMo Instruct.*

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

Lowest available price from all providers

Fri May 15 2026 • llm-stats.com

Mistral NeMo Instruct

Input tokens$0.15

Output tokens$0.15

Best providerGoogle

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

8.0B diff

Mistral NeMo Instruct has 8.0B more parameters than Qwen3 VL 4B Thinking, making it 200.0% larger.

Mistral NeMo Instruct

12.0Bparameters

Qwen3 VL 4B Thinking

4.0Bparameters

12.0B

Mistral NeMo Instruct

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 Mistral NeMo Instruct's 128,000 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while Mistral NeMo Instruct is limited to 128,000 tokens.

Mistral NeMo Instruct

Input128,000 tokens

Output128,000 tokens

Qwen3 VL 4B Thinking

Input262,144 tokens

Output262,144 tokens

Fri May 15 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 4B Thinking supports multimodal inputs, whereas Mistral NeMo Instruct does not.

Qwen3 VL 4B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

Mistral NeMo Instruct

Text

Images

Audio

Video

Qwen3 VL 4B Thinking

Text

Images

Audio

Video

License

Usage and distribution terms

Both models are licensed under Apache 2.0.

Both models share the same licensing terms, providing consistent usage rights.

Mistral NeMo Instruct

Apache 2.0

Open weights

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Mistral NeMo Instruct was released on 2024-07-18, while Qwen3 VL 4B Thinking was released on 2025-09-22.

Qwen3 VL 4B Thinking is 14 months newer than Mistral NeMo Instruct.

Mistral NeMo Instruct

Jul 18, 2024

1.8 years ago

Qwen3 VL 4B Thinking

Sep 22, 2025

7 months ago

1.2yr newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Mistral NeMo Instruct is available from Google, Mistral AI. Qwen3 VL 4B Thinking is available from DeepInfra.

Mistral NeMo Instruct

Google

Input Price:Input: $0.15/1MOutput Price:Output: $0.15/1M

Mistral

Input Price:Input: $0.15/1MOutput Price:Output: $0.15/1M

Qwen3 VL 4B Thinking

Deepinfra

Input Price:Input: $0.10/1MOutput Price:Output: $1.00/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Mistral NeMo Instruct

View details

Mistral AI

Less expensive output tokens

Qwen3 VL 4B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Supports multimodal inputs

Less expensive input tokens

Higher MMLU score (81.5% vs 68.0%)

Detailed Comparison

AI Model Comparison Table
Feature	Mistral NeMo Instruct	Qwen3 VL 4B Thinking

FAQ

Common questions about Mistral NeMo Instruct vs Qwen3 VL 4B Thinking.

Which is better, Mistral NeMo Instruct or Qwen3 VL 4B Thinking?

Qwen3 VL 4B Thinking significantly outperforms across most benchmarks. Mistral NeMo Instruct is made by Mistral AI and Qwen3 VL 4B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Mistral NeMo Instruct compare to Qwen3 VL 4B Thinking in benchmarks?

Mistral NeMo Instruct scores HellaSwag: 83.5%, Winogrande: 76.8%, TriviaQA: 73.8%, CommonSenseQA: 70.4%, MMLU: 68.0%. Qwen3 VL 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.

Is Mistral NeMo Instruct cheaper than Qwen3 VL 4B Thinking?

Qwen3 VL 4B Thinking is 1.5x cheaper for input tokens. Mistral NeMo Instruct costs $0.15/M input and $0.15/M output via google. Qwen3 VL 4B Thinking costs $0.10/M input and $1.00/M output via deepinfra.

What are the context window sizes for Mistral NeMo Instruct and Qwen3 VL 4B Thinking?

Mistral NeMo Instruct supports 128K 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 Mistral NeMo Instruct and Qwen3 VL 4B Thinking?

Key differences include context window (128K vs 262K), input pricing ($0.15 vs $0.10/M), multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.

Who makes Mistral NeMo Instruct and Qwen3 VL 4B Thinking?

Mistral NeMo Instruct is developed by Mistral AI and Qwen3 VL 4B Thinking is developed by Alibaba Cloud / Qwen Team.