Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3 235B A22B (Reasoning) | Gemini 3.5 Flash (high) |
|---|---|---|
| Input ($/M tokens) | $0.7 | $1.5 |
| Output ($/M tokens) | $8.4 | $9 |
Verdict. Gemini 3.5 Flash (high) 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 mid-tier bracket for output-token pricing. At 0.9× the per-million-token cost, Qwen3 235B A22B (Reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3 235B A22B (Reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Qwen3 235B A22B (Reasoning) is strongest on Intelligence Index (13.5). Gemini 3.5 Flash (high) leads on Coding Index (70.1), Intelligence Index (52.0).
Speed. On throughput, Gemini 3.5 Flash (high) generates tokens at 171 tok/s versus 61 tok/s — about 64% faster. On time-to-first-token, Qwen3 235B A22B (Reasoning) responds in 2730ms vs 27290ms, which matters most for chat-style UIs.
Provider. Alibaba and Google sell to overlapping but distinct developer audiences: Alibaba tends to ship frontier reasoning models with premium positioning, while Google 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 235B A22B (Reasoning) costs $147.00 ($1764/year); Gemini 3.5 Flash (high) costs $180.00 ($2160/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3 235B A22B (Reasoning) ≈ $20.30/run, Gemini 3.5 Flash (high) ≈ $25.50/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3 235B A22B (Reasoning) ≈ $980/run, Gemini 3.5 Flash (high) ≈ $1200/run. Qwen3 235B A22B (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 235B A22B (Reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $2.63/M tokens vs $3.38/M for Gemini 3.5 Flash (high).
Gemini 3.5 Flash (high) wins 2 out of 12 benchmarks compared to 0 for Qwen3 235B A22B (Reasoning). See the detailed benchmark chart above for per-category results.
Gemini 3.5 Flash (high) generates tokens faster at 171 tok/s vs 61 tok/s. Qwen3 235B A22B (Reasoning) also has lower time-to-first-token (2.73s vs 27.29s).
Choose based on your priorities: Qwen3 235B A22B (Reasoning) for lower cost, Gemini 3.5 Flash (high) for stronger benchmark performance, and Gemini 3.5 Flash (high) for faster generation. For latency-sensitive apps, check the TTFT comparison above.