# Meta Llama 3.1 405B Instruct vs Qwen 3.5

On provider list prices, Qwen 3.5 costs $0.60 per million input tokens against $3.50 for Meta Llama 3.1 405B Instruct: 5.8x apart. Output is $3.60 against $3.50. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.1 405B Instruct | Qwen 3.5 |
| --- | --- | --- |
| Lab | Meta | Qwen |
| Access | Open weights | Open weights |
| Context window | 4K tokens | 256K tokens |
| List price, input | $3.50 / M tokens | $0.60 / M tokens |
| List price, output | $3.50 / M tokens | $3.60 / M tokens |
| Cached input | n/a | $0.35 / M tokens |
| License | Llama community | Apache 2.0 |
| Fine-tunable | Yes | Yes |

Specifications and provider list prices from the Allocate catalog, checked 2026-07-08. Billed price is list plus the 7% transaction fee.

## What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $1,980 a month on Qwen 3.5 and $5,425 on Meta Llama 3.1 405B Instruct at list: a gap of $3,445, or 2.7x.

Qwen 3.5 reads 256K tokens per request against 4K for Meta Llama 3.1 405B Instruct, 64.0x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3.1 405B Instruct for

- Training toward a model you own

## Choose Qwen 3.5 for

- Multilingual support agents
- Translation-adjacent workflows
- Fine-tuning under Apache 2.0

## Common questions

### Which is cheaper, Meta Llama 3.1 405B Instruct or Qwen 3.5?

Qwen 3.5, on this workload shape. At list prices it is $0.60/$3.60 per million tokens in and out against $3.50/$3.50 for Meta Llama 3.1 405B Instruct. Billed on Allocate: $0.64/$3.85 against $3.75/$3.75, list plus 7%.

### Which has the bigger context window?

Qwen 3.5: 262,144 tokens (256K) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.

### Can I fine-tune Meta Llama 3.1 405B Instruct or Qwen 3.5?

Both publish open weights (Meta Llama 3.1 405B Instruct: Llama community; Qwen 3.5: Apache 2.0), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

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[HTML page](https://allocate.network/compare/meta-llama-3-1-405b-instruct-vs-qwen-3-5) · [Meta Llama 3.1 405B Instruct](https://allocate.network/models/meta-llama-3-1-405b-instruct.md) · [Qwen 3.5](https://allocate.network/models/qwen-3-5.md) · [Machine-readable catalog](https://allocate.network/catalog.json)