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Llama 3.1 70B Instruct vs Qwen3 VL 32B Thinking

Qwen3 VL 32B Thinking leads the LLM Stats Score 23.5 to 7.6.

Meta · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Qwen3 VL 32B Thinking leads the overall LLM Stats Score 23.5 to 7.6, ranking #189 overall.

In the 4 individual benchmarks reported for both models, Qwen3 VL 32B Thinking wins 4; this is a narrower head-to-head signal than the composite indexes.

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

Choose Llama 3.1 70B Instruct

  • you want predictable pricing at $0.20/M input and $0.20/M output

Choose Qwen3 VL 32B Thinking

  • overall performance matters — it scores 23.5 and ranks #189 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 4 of 4 exact shared results
  • you want the most recent training data — it shipped Sep 2025

At a glance

The differences that matter most.

Llama 3.1 70B Instruct
Meta
Qwen3 VL 32B Thinking
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
7.6
#302
23.5
#189
Reasoning
6.3
#304
24.7
#170
Cost, coverage & limits
Benchmark wins
0 of 4
4 of 4
Input price
$0.20 / M
— / M
Output price
$0.20 / M
— / M
Context window
128,000
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
Llama 3.1 70B Instruct
Qwen3 VL 32B Thinking
Math
11.4#246
25.4#114
Finance
9.3#151
27.8#37
Legal
9.3#134
27.1#28
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

18 reported for Llama 3.1 70B Instruct · 47 for Qwen3 VL 32B Thinking

4 shared

Llama 3.1 70B Instruct outperforms in 0 benchmarks, while Qwen3 VL 32B Thinking is better at 4 benchmarks (GPQA, IFEval, MMLU, MMLU-Pro).

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Fri Oct 16 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

37.0B diff

Llama 3.1 70B Instruct has 37.0B more parameters than Qwen3 VL 32B Thinking, making it 112.1% larger.

Meta
Llama 3.1 70B Instruct
70.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
70.0B
Llama 3.1 70B Instruct
33.0B
Qwen3 VL 32B Thinking

Context Window

Maximum input and output token capacity

Only Llama 3.1 70B Instruct specifies input context (128,000 tokens). Only Llama 3.1 70B Instruct specifies output context (128,000 tokens).

Meta
Llama 3.1 70B Instruct
Input128,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Fri Oct 16 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Qwen3 VL 32B Thinking supports multimodal inputs, whereas Llama 3.1 70B Instruct does not.

Qwen3 VL 32B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

Llama 3.1 70B Instruct

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Llama 3.1 70B Instruct is licensed under Llama 3.1 Community License, while Qwen3 VL 32B Thinking uses Apache 2.0.

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

Llama 3.1 70B Instruct

Llama 3.1 Community License

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Llama 3.1 70B Instruct was released on 2024-07-23, while Qwen3 VL 32B Thinking was released on 2025-09-22.

Qwen3 VL 32B Thinking is 14 months newer than Llama 3.1 70B Instruct.

Llama 3.1 70B Instruct

Jul 23, 2024

2.2 years ago

Qwen3 VL 32B Thinking

Sep 22, 2025

1.1 years 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

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against Llama 3.1 70B Instruct and Qwen3 VL 32B Thinking side-by-side, then vote on the output you prefer.

Llama 3.1 70B Instruct
✓ Preferred
Qwen3 VL 32B Thinking
Open in Playground

FAQ

Common questions about Llama 3.1 70B Instruct vs Qwen3 VL 32B Thinking.

Which is better, Llama 3.1 70B Instruct or Qwen3 VL 32B Thinking?

Qwen3 VL 32B Thinking leads the LLM Stats Score 23.5 to 7.6. Llama 3.1 70B Instruct is made by Meta and Qwen3 VL 32B 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 Llama 3.1 70B Instruct compare to Qwen3 VL 32B Thinking in benchmarks?

Llama 3.1 70B Instruct scores GSM-8K (CoT): 95.1%, ARC-C: 94.8%, API-Bank: 90.0%, IFEval: 87.5%, Multilingual MGSM (CoT): 86.9%. Qwen3 VL 32B Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%.

What are the context window sizes for Llama 3.1 70B Instruct and Qwen3 VL 32B Thinking?

Llama 3.1 70B Instruct supports 128K tokens and Qwen3 VL 32B Thinking supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Llama 3.1 70B Instruct and Qwen3 VL 32B Thinking?

Key differences include LLM Stats Score (7.6 vs 23.5), multimodal support (no vs yes), licensing (Llama 3.1 Community License vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Llama 3.1 70B Instruct and Qwen3 VL 32B Thinking?

Llama 3.1 70B Instruct is developed by Meta and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.

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