Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3 8B (Non-reasoning) | Llama 3.3 Instruct 70B |
|---|---|---|
| Input ($/M tokens) | $0.18 | $0.66 |
| Output ($/M tokens) | $0.7 | $0.72 |
Verdict. Llama 3.3 Instruct 70B takes the aggregate benchmark matchup 2–0 across 2 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, Qwen3 8B (Non-reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3 8B (Non-reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Qwen3 8B (Non-reasoning) is strongest on Intelligence Index (4.8). Llama 3.3 Instruct 70B leads on Coding Index (11.9), Intelligence Index (9.3).
Speed. On throughput, Llama 3.3 Instruct 70B generates tokens at 84 tok/s versus 39 tok/s — about 53% faster. On time-to-first-token, Llama 3.3 Instruct 70B responds in 1610ms vs 3770ms, which matters most for chat-style UIs.
Provider. Alibaba and Meta sell to overlapping but distinct developer audiences: Alibaba tends to ship frontier reasoning models with premium positioning, while Meta 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): Qwen3 8B (Non-reasoning) costs $15.90 ($191/year); Llama 3.3 Instruct 70B costs $30.60 ($367/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3 8B (Non-reasoning) ≈ $2.30/run, Llama 3.3 Instruct 70B ≈ $4.74/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3 8B (Non-reasoning) ≈ $106/run, Llama 3.3 Instruct 70B ≈ $204/run. Qwen3 8B (Non-reasoning) 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.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
Qwen3 8B (Non-reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.31/M tokens vs $0.68/M for Llama 3.3 Instruct 70B.
Llama 3.3 Instruct 70B wins 2 out of 12 benchmarks compared to 0 for Qwen3 8B (Non-reasoning). See the detailed benchmark chart above for per-category results.
Llama 3.3 Instruct 70B generates tokens faster at 84 tok/s vs 39 tok/s. However, Llama 3.3 Instruct 70B has lower time-to-first-token (1.61s vs 3.77s).
Choose based on your priorities: Qwen3 8B (Non-reasoning) for lower cost, Llama 3.3 Instruct 70B for stronger benchmark performance, and Llama 3.3 Instruct 70B for faster generation. For latency-sensitive apps, check the TTFT comparison above.