Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 4 Maverick | Qwen3 235B A22B 2507 Instruct |
|---|---|---|
| Input ($/M tokens) | $0.26 | $0.23 |
| Output ($/M tokens) | $0.91 | $0.92 |
Verdict. Llama 4 Maverick and Qwen3 235B A22B 2507 Instruct split the benchmark comparison evenly at 1–1. The tiebreaker here is price, speed, and what you actually run them on.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Llama 4 Maverick is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Llama 4 Maverick makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 4 Maverick is strongest on Coding Index (16.3), Intelligence Index (14.5). Qwen3 235B A22B 2507 Instruct leads on Intelligence Index (18.4).
Speed. On throughput, Llama 4 Maverick generates tokens at 96 tok/s versus 56 tok/s — about 41% faster. On time-to-first-token, Llama 4 Maverick responds in 930ms vs 2390ms, which matters most for chat-style UIs.
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 4 Maverick costs $21.45 ($257/year); Qwen3 235B A22B 2507 Instruct costs $20.70 ($248/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 4 Maverick ≈ $3.12/run, Qwen3 235B A22B 2507 Instruct ≈ $2.99/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 4 Maverick ≈ $143/run, Qwen3 235B A22B 2507 Instruct ≈ $138/run. Qwen3 235B A22B 2507 Instruct 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 235B A22B 2507 Instruct is cheaper overall. Its blended price (3:1 input/output ratio) is $0.40/M tokens vs $0.42/M for Llama 4 Maverick.
It's a tie — both models win 1 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.
Llama 4 Maverick generates tokens faster at 96 tok/s vs 56 tok/s. Llama 4 Maverick also has lower time-to-first-token (0.93s vs 2.39s).
Choose based on your priorities: Qwen3 235B A22B 2507 Instruct for lower cost, both perform similarly on benchmarks, and Llama 4 Maverick for faster generation. For latency-sensitive apps, check the TTFT comparison above.