Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 3 Instruct 70B | Qwen3 235B A22B (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.65 | $0.7 |
| Output ($/M tokens) | $2.75 | $2.8 |
Verdict. Qwen3 235B A22B (Non-reasoning) takes the aggregate benchmark matchup 1–0 across 1 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Llama 3 Instruct 70B is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Llama 3 Instruct 70B makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 3 Instruct 70B is strongest on Intelligence Index (3.1). Qwen3 235B A22B (Non-reasoning) leads on Intelligence Index (10.8).
Speed. Speed data is incomplete for this pair; benchmark and price should decide.
Provider. Meta and Alibaba sell to overlapping but distinct developer audiences: Meta tends to ship frontier reasoning models with premium positioning, while Alibaba often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Llama 3 Instruct 70B costs $60.75 ($729/year); Qwen3 235B A22B (Non-reasoning) costs $63.00 ($756/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 3 Instruct 70B ≈ $8.75/run, Qwen3 235B A22B (Non-reasoning) ≈ $9.10/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 3 Instruct 70B ≈ $405/run, Qwen3 235B A22B (Non-reasoning) ≈ $420/run. Llama 3 Instruct 70B becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Data from Artificial Analysis API — 12 benchmarks
Llama 3 Instruct 70B is cheaper overall. Its blended price (3:1 input/output ratio) is $1.18/M tokens vs $1.23/M for Qwen3 235B A22B (Non-reasoning).
Qwen3 235B A22B (Non-reasoning) wins 1 out of 12 benchmarks compared to 0 for Llama 3 Instruct 70B. See the detailed benchmark chart above for per-category results.
Qwen3 235B A22B (Non-reasoning) generates tokens faster at 59 tok/s vs — tok/s. However, Qwen3 235B A22B (Non-reasoning) has lower time-to-first-token (2.65s vs —s).
Choose based on your priorities: Llama 3 Instruct 70B for lower cost, Qwen3 235B A22B (Non-reasoning) for stronger benchmark performance, and Qwen3 235B A22B (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.