Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3 235B A22B 2507 Instruct | Agnes 2.5 Pro Alpha |
|---|---|---|
| Input ($/M tokens) | $0.23 | $0.45 |
| Output ($/M tokens) | $0.92 | $0.9 |
Verdict. Agnes 2.5 Pro Alpha 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, Agnes 2.5 Pro Alpha is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Agnes 2.5 Pro Alpha makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Qwen3 235B A22B 2507 Instruct is strongest on Intelligence Index (18.4). Agnes 2.5 Pro Alpha leads on Coding Index (58.8), Intelligence Index (39.7).
Speed. On throughput, Agnes 2.5 Pro Alpha generates tokens at 130 tok/s versus 56 tok/s — about 57% faster. On time-to-first-token, Agnes 2.5 Pro Alpha responds in 1920ms vs 2390ms, which matters most for chat-style UIs.
Provider. Alibaba and Sapiens AI sell to overlapping but distinct developer audiences: Alibaba tends to ship frontier reasoning models with premium positioning, while Sapiens AI 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 2507 Instruct costs $20.70 ($248/year); Agnes 2.5 Pro Alpha costs $27.00 ($324/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3 235B A22B 2507 Instruct ≈ $2.99/run, Agnes 2.5 Pro Alpha ≈ $4.05/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3 235B A22B 2507 Instruct ≈ $138/run, Agnes 2.5 Pro Alpha ≈ $180/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 $0.56/M for Agnes 2.5 Pro Alpha.
Agnes 2.5 Pro Alpha wins 2 out of 12 benchmarks compared to 0 for Qwen3 235B A22B 2507 Instruct. See the detailed benchmark chart above for per-category results.
Agnes 2.5 Pro Alpha generates tokens faster at 130 tok/s vs 56 tok/s. However, Agnes 2.5 Pro Alpha has lower time-to-first-token (1.92s vs 2.39s).
Choose based on your priorities: Qwen3 235B A22B 2507 Instruct for lower cost, Agnes 2.5 Pro Alpha for stronger benchmark performance, and Agnes 2.5 Pro Alpha for faster generation. For latency-sensitive apps, check the TTFT comparison above.