Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | PALM-2 | Qwen3.5 Omni Plus |
|---|---|---|
| Input ($/M tokens) | — | $0.4 |
| Output ($/M tokens) | — | $4.8 |
Verdict. PALM-2 and Qwen3.5 Omni Plus split the benchmark comparison evenly at 1–1. The tiebreaker here is price, speed, and what you actually run them on.
Pricing. Pricing varies significantly between these models — check the table above for the exact per-token rates. Many production workloads actually surface input-token cost (retrieval-augmented prompts, code-context windows), so factor both directions.
Strengths. PALM-2 is strongest on Coding Index (4.6), Intelligence Index (2.8). Qwen3.5 Omni Plus leads on Intelligence Index (31.3).
Speed. Speed data is incomplete for this pair; benchmark and price should decide.
Provider. Google and Alibaba sell to overlapping but distinct developer audiences: Google 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.
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
Qwen3.5 Omni Plus is cheaper overall. Its blended price (3:1 input/output ratio) is $1.50/M tokens vs $—/M for PALM-2.
It's a tie — both models win 1 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.
Qwen3.5 Omni Plus generates tokens faster at 49 tok/s vs — tok/s. However, Qwen3.5 Omni Plus has lower time-to-first-token (2.51s vs —s).
Choose based on your priorities: Qwen3.5 Omni Plus for lower cost, both perform similarly on benchmarks, and Qwen3.5 Omni Plus for faster generation. For latency-sensitive apps, check the TTFT comparison above.