Model Comparison

Qwen3.5-397B-A17B vs Llama 3.1 405B Instruct

Qwen3.5-397B-A17B significantly outperforms across most benchmarks. Llama 3.1 405B Instruct is 1.5x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

Qwen3.5-397B-A17B outperforms in 3 benchmarks (GPQA, IFEval, MMLU-Pro), while Llama 3.1 405B Instruct is better at 0 benchmarks.

Qwen3.5-397B-A17B significantly outperforms across most benchmarks.

Tue Apr 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Llama 3.1 405B Instruct costs less

For input processing, Qwen3.5-397B-A17B ($0.60/1M tokens) is 1.5x cheaper than Llama 3.1 405B Instruct ($0.89/1M tokens).

For output processing, Qwen3.5-397B-A17B ($3.60/1M tokens) is 4.0x more expensive than Llama 3.1 405B Instruct ($0.89/1M tokens).

In conclusion, Qwen3.5-397B-A17B is more expensive than Llama 3.1 405B Instruct.*

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

Lowest available price from all providers
Tue Apr 14 2026 • llm-stats.com
Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
Input tokens$0.60
Output tokens$3.60
Best providerNovita
Meta
Llama 3.1 405B Instruct
Input tokens$0.89
Output tokens$0.89
Best providerLambda
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

8.0B diff

Llama 3.1 405B Instruct has 8.0B more parameters than Qwen3.5-397B-A17B, making it 2.0% larger.

Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
397.0Bparameters
Meta
Llama 3.1 405B Instruct
405.0Bparameters
397.0B
Qwen3.5-397B-A17B
405.0B
Llama 3.1 405B Instruct

Context Window

Maximum input and output token capacity

Qwen3.5-397B-A17B accepts 262,144 input tokens compared to Llama 3.1 405B Instruct's 128,000 tokens. Llama 3.1 405B Instruct can generate longer responses up to 128,000 tokens, while Qwen3.5-397B-A17B is limited to 64,000 tokens.

Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
Input262,144 tokens
Output64,000 tokens
Meta
Llama 3.1 405B Instruct
Input128,000 tokens
Output128,000 tokens
Tue Apr 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3.5-397B-A17B supports multimodal inputs, whereas Llama 3.1 405B Instruct does not.

Qwen3.5-397B-A17B can handle both text and other forms of data like images, making it suitable for multimodal applications.

Qwen3.5-397B-A17B

Text
Images
Audio
Video

Llama 3.1 405B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Qwen3.5-397B-A17B is licensed under Apache 2.0, while Llama 3.1 405B Instruct uses Llama 3.1 Community License.

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

Qwen3.5-397B-A17B

Apache 2.0

Open weights

Llama 3.1 405B Instruct

Llama 3.1 Community License

Open weights

Release Timeline

When each model was launched

Qwen3.5-397B-A17B was released on 2026-02-16, while Llama 3.1 405B Instruct was released on 2024-07-23.

Qwen3.5-397B-A17B is 19 months newer than Llama 3.1 405B Instruct.

Qwen3.5-397B-A17B

Feb 16, 2026

1 months ago

1.6yr newer
Llama 3.1 405B Instruct

Jul 23, 2024

1.7 years ago

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

Qwen3.5-397B-A17B is available from Novita. Llama 3.1 405B Instruct is available from Lambda, DeepInfra, Fireworks, Bedrock, Together, Hyperbolic, Google, Replicate.

Qwen3.5-397B-A17B

novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $3.60/1M

Llama 3.1 405B Instruct

lambda logo
Lambda
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
deepinfra logo
Deepinfra
Input Price:Input: $1.79/1MOutput Price:Output: $1.79/1M
fireworks logo
Fireworks
Input Price:Input: $3.00/1MOutput Price:Output: $3.00/1M
bedrock logo
AWS Bedrock
Input Price:Input: $3.00/1MOutput Price:Output: $3.00/1M
together logo
Together
Input Price:Input: $3.50/1MOutput Price:Output: $3.50/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $4.00/1MOutput Price:Output: $4.00/1M
google logo
Google
Input Price:Input: $5.00/1MOutput Price:Output: $16.00/1M
replicate logo
Replicate
Input Price:Input: $9.50/1MOutput Price:Output: $9.50/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Alibaba Cloud / Qwen Team

Qwen3.5-397B-A17B

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)
Supports multimodal inputs
Less expensive input tokens
Higher GPQA score (88.4% vs 50.7%)
Higher IFEval score (92.6% vs 88.6%)
Higher MMLU-Pro score (87.8% vs 73.3%)
Meta
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Alibaba Cloud / Qwen Team
Qwen3.5-397B-A17B
Meta
Llama 3.1 405B Instruct

FAQ

Common questions about Qwen3.5-397B-A17B vs Llama 3.1 405B Instruct

Qwen3.5-397B-A17B significantly outperforms across most benchmarks. Qwen3.5-397B-A17B is made by Alibaba Cloud / Qwen Team and Llama 3.1 405B Instruct is made by Meta. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Qwen3.5-397B-A17B scores MMLU-Redux: 94.9%, HMMT 2025: 94.8%, C-Eval: 93.0%, HMMT25: 92.7%, IFEval: 92.6%. Llama 3.1 405B Instruct scores ARC-C: 96.9%, GSM8k: 96.8%, API-Bank: 92.0%, Multilingual MGSM (CoT): 91.6%, HumanEval: 89.0%.
Qwen3.5-397B-A17B is 1.5x cheaper for input tokens. Qwen3.5-397B-A17B costs $0.60/M input and $3.60/M output via novita. Llama 3.1 405B Instruct costs $0.89/M input and $0.89/M output via lambda.
Qwen3.5-397B-A17B supports 262K tokens and Llama 3.1 405B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (262K vs 128K), input pricing ($0.60 vs $0.89/M), multimodal support (yes vs no), licensing (Apache 2.0 vs Llama 3.1 Community License). See the full comparison above for benchmark-by-benchmark results.
Qwen3.5-397B-A17B is developed by Alibaba Cloud / Qwen Team and Llama 3.1 405B Instruct is developed by Meta.