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Llama 3.3 70B vs Qwen 3 235B

A straight cost comparison of Llama 3.3 70B (Meta (Llama)) and Qwen 3 235B (Alibaba (Qwen)), per 1,000,000 tokens, verified November 2026.

Rate-by-rate comparison

For per-token metrics, the lower number wins. For context window and batch support, the higher or broader option wins. A dash means the provider does not publish that rate: a missing cache-write fee means writes are not billed per token, while a missing cached-input rate means cache hits bill at the full input rate.

Blended cost: 1M input + 250K output

Ten workloads, two bills

The same two models, priced across ten workloads that differ only in their input:output ratio. Nothing else changes — no retries, no system prompt, no middleware.

Llama 3.3 70B wins 1 of 10, Qwen 3 235B wins 9 of 10. The winner is set by the token mix, not by the headline rate.

Where the two bills cross over

The two models split the token mix. Qwen 3 235B is cheaper per input token ($0.50 vs $0.88 per million), while Llama 3.3 70B is cheaper per output token ($0.88 vs $2). The bills cross at 0.34 output tokens per input token.

Below that ratio — classification, extraction, retrieval, summarisation — Qwen 3 235B wins. Above it — translation, long-form generation, agent loops that write more than they read — Llama 3.3 70B wins. Most production workloads sit between 0.10 and 1.00 output tokens per input token, which is why Qwen 3 235B is the cheaper answer on a real invoice even when the sticker rate says otherwise.

Cost at four scales

The same support-chat workload (4K input, 600 output per request) at four monthly volumes.

What caching does to each bill

Caching only helps the part of the prompt that repeats. This is the support-chat workload (1,000 requests, 4,000 input tokens each) with an increasing share of that prompt served from a warm cache, written once on the first request.

Both models publish a cached-read rate, so the discount is comparable: Llama 3.3 70B reads cache at $0.088 per million, Qwen 3 235B at $0.05.

Which one should you pick?

Llama 3.3 70B wins on translation, while Qwen 3 235B wins on classification, data extraction, support chat, code review, summarisation, agent loop, long-document qa, cached rag, nightly batch.

Beyond price, the two differ on input rate ($0.88 vs $0.50); output rate ($0.88 vs $2); cached-input rate ($0.088 vs $0.05); context window (128,000 vs 131,072).

Batch mode is available on Llama 3.3 70B and Qwen 3 235B, which halves the nightly job. Both publish cached-input discounts, so a stable system prompt is worth caching on either.

FAQ

Is Llama 3.3 70B cheaper than Qwen 3 235B?

On a blended 1M-input-plus-250K-output workload, Qwen 3 235B is cheaper by 9% ($1 vs $1.10). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.

Which handles longer prompts, Llama 3.3 70B or Qwen 3 235B?

Qwen 3 235B has the larger context window (131,072 vs 128,000 tokens). Neither model publishes a long-context surcharge.

Do both models support prompt caching?

Llama 3.3 70B bills cached reads at $0.088 per 1M; Qwen 3 235B at $0.05.

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