# Meta Llama 3.1 405B Instruct vs Qwen3.7 Max

On provider list prices, Qwen3.7 Max costs $1.25 per million input tokens against $3.50 for Meta Llama 3.1 405B Instruct: 2.8x apart. Output is $3.75 against $3.50. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Meta Llama 3.1 405B Instruct | Qwen3.7 Max |
| --- | --- | --- |
| Lab | Meta | Qwen |
| Access | Open weights | API only |
| Context window | 4K tokens | 1M tokens |
| List price, input | $3.50 / M tokens | $1.25 / M tokens |
| List price, output | $3.50 / M tokens | $3.75 / M tokens |
| Cached input | n/a | $0.13 / M tokens |
| License | Llama community | Proprietary API |
| Fine-tunable | Yes | No |

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 $2,813 a month on Qwen3.7 Max and $5,425 on Meta Llama 3.1 405B Instruct at list: a gap of $2,613, or 1.9x.

Qwen3.7 Max reads 1M tokens per request against 4K for Meta Llama 3.1 405B Instruct, 244.1x the window. That decides which one can take whole documents without splitting them.

## Choose Meta Llama 3.1 405B Instruct for

- Open weights you can fine-tune and own

## Choose Qwen3.7 Max for

- The lower list price ($1.25 in / $3.75 out per M tokens)
- The longer context window (1M vs 4K tokens)
- Published cached-input pricing ($0.13 per M tokens)

## Common questions

### Which is cheaper, Meta Llama 3.1 405B Instruct or Qwen3.7 Max?

Qwen3.7 Max, on this workload shape. At list prices it is $1.25/$3.75 per million tokens in and out against $3.50/$3.50 for Meta Llama 3.1 405B Instruct. Billed on Allocate: $1.34/$4.01 against $3.75/$3.75, list plus 7%.

### Which has the bigger context window?

Qwen3.7 Max: 1,000,000 tokens (1M) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.

### Can I fine-tune Meta Llama 3.1 405B Instruct or Qwen3.7 Max?

Meta Llama 3.1 405B Instruct publishes open weights (Llama community) and can be fine-tuned on your own data. Qwen3.7 Max is a closed model served over API; its weights are not available.

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[HTML page](https://allocate.network/compare/meta-llama-3-1-405b-instruct-vs-qwen-qwen3-7-max) · [Meta Llama 3.1 405B Instruct](https://allocate.network/models/meta-llama-3-1-405b-instruct.md) · [Qwen3.7 Max](https://allocate.network/models/qwen-qwen3-7-max.md) · [Machine-readable catalog](https://allocate.network/catalog.json)