Gemini 3.5 Flash (Google) scores 55.3 on the quality index while generating 227 tokens per second at $3.38/M tokens. That combination doesn't exist anywhere else in the current model landscape. The closest competitor on speed is Gemini 3.1 Pro Preview at 135 tok/s, which costs $4.50/M and scores 57.2. Every GPT-5.5 variant costs $11.25/M and runs between 59–63 tok/s. If your workload is latency-sensitive, cost-constrained, or both, Gemini 3.5 Flash is the model to evaluate first.
What 227 tok/s actually means for production workloads
Raw throughput numbers matter most when they compound. At 227 tok/s, Gemini 3.5 Flash generates a 1,000-token response in about 4.4 seconds. GPT-5.5 (high) takes roughly 15.9 seconds for the same output at 63 tok/s. GPT-5.5 (medium) is even slower at 59 tok/s.
For user-facing applications—chatbots, autocomplete, search summarization—that difference is the gap between a responsive product and a sluggish one. For batch inference pipelines processing millions of requests, it's the difference between a 4-hour job and a 15-hour one. And because Gemini 3.5 Flash costs $3.38/M tokens versus $11.25/M for either GPT-5.5 variant, you're paying roughly 70% less per token while finishing 3.6× faster.
The quality gap is real but narrower than the price gap
I won't pretend 55.3 equals 58.9. GPT-5.5 (high) does score 3.6 points higher on the quality index, and for tasks where marginal quality improvements translate directly into business value—legal document review, medical summarization, complex multi-step reasoning—that gap matters.
But look at the economics. GPT-5.5 (high) costs 3.3× more per million tokens for a 6.5% quality improvement. GPT-5.5 (medium) scores 56.7, only 1.4 points above Gemini 3.5 Flash, yet still costs 3.3× more and runs at roughly one-quarter the speed. The medium tier is the hardest to justify: you're paying the full GPT-5.5 price for quality that barely separates from Flash.
Quality per dollar tells a stark story. Gemini 3.5 Flash delivers 3.1× more quality per dollar than GPT-5.5 (high). MiMo-V2.5-Pro (Xiaomi) pushes even further at 35.87 quality per dollar, but at 54 tok/s and a 53.8 quality score, it occupies a different niche: high-volume, quality-tolerant batch work where every fraction of a cent matters.
Where Gemini 3.5 Flash falls short
Speed and cost advantages erode when your failure mode is quality-driven retries. If a model produces an unusable response 10% of the time and you need to re-run, your effective cost includes those retries. A model scoring 58.9 that fails less often on complex prompts could end up cheaper in practice than a 55.3 model that needs more passes.
I also can't evaluate Gemini 3.5 Flash on coding or structured output specifically—the quality index is a composite, and the underlying benchmark breakdown isn't available here. If your pipeline depends heavily on structured JSON output or multi-file code generation, the aggregate score may not reflect your actual experience.
Qwen3.7 Max (Alibaba) is another wildcard. It scores 56.6—above Flash—and it's open source. But with no published pricing or speed data, it's impossible to make an operational comparison. If you can self-host and your infrastructure costs work out below $3.38/M equivalent, Qwen3.7 Max could be the better play. That's a meaningful "if."
The GPT-5.5 fragmentation problem
OpenAI now offers three GPT-5.5 tiers: the base at 60.2 quality, high at 58.9, and medium at 56.7. All three cost $11.25/M tokens. The speed differences are marginal (59–63 tok/s). This pricing structure is puzzling. Medium delivers 5.8% less quality than the base model for the same price and nearly the same speed. Unless there's a latency or rate-limit advantage not captured in throughput numbers, it's difficult to construct a scenario where medium is the right choice over base.
This fragmentation makes the value proposition of the GPT-5.5 family harder to articulate. You're choosing between three quality tiers at identical cost, while Google offers a model that scores within 1.4 points of GPT-5.5 (medium) at less than a third of the price and nearly 4× the throughput.
Who should use what
For latency-critical, cost-sensitive production: Gemini 3.5 Flash. The 227 tok/s throughput and $3.38/M price point make it the default choice for real-time applications, high-volume summarization, and any pipeline where you're optimizing for cost at acceptable quality.
For maximum quality regardless of budget: GPT-5.5 base (60.2) or GPT-5.5 (high) at 58.9, both at $11.25/M. Choose these when the task demands the highest possible composite quality and you can absorb the cost and latency.
For budget batch processing: MiMo-V2.5-Pro at $1.50/M tokens. The 53.8 quality score is adequate for classification, extraction, and other tasks with well-defined success criteria. The 54 tok/s speed is slow, but batch jobs care about cost per token, not time to first token.
For self-hosting: Qwen3.7 Max at 56.6 quality is the strongest open-source option in this tier, but you need your own benchmarks on your own hardware before committing.
Use the LLM Selector to filter by your specific speed, quality, and price constraints, or browse the full rankings on Explore.