Why a Within-Tier Comparison Beats Cross-Tier
Once you've narrowed a model shortlist on the main TokenRate calculator, the Compare Prices side-by-side view is where you stack them for a decision. Each row shows the provider, the model ID (the one you'd paste into your SDK), per-1M input and output costs, the context window, and the blended quality score. Once you've picked your tier — Fast — the next question is which **specific** Fast model. Cross-tier comparisons (flagship vs fast) are usually a budgeting question. Within-tier comparisons are routing questions: "of the models built for the same workload class, which is the best fit for mine?" This guide grids Claude Haiku 4.5, GPT-4o mini, Gemini 2.5 Flash, Gemini 2.5 Flash-Lite, Llama 4 Scout, Mistral Small side-by-side in /tools/compare-prices. Related reading: quality per dollar LLM ranking 2026, LLM color-coded quality badges explained, and why the cheapest LLM isn't always the best value.
Fast Tier Defined
Fast tier on TokenRate means: fast tier is built for high-volume throughput at the lowest per-token rate the provider offers. Input prices typically span $0.075 to $1.00 per 1M tokens within the tier. Quality scores span 52 to 68. So even within the tier, the Value column will diverge — which is the whole point of comparing within-tier instead of just defaulting to whichever model is most familiar.
The Fast Models, Compared
**Claude Haiku 4.5** (Anthropic): $1.00 / $5.00, 200K ctx, Q65, value 65. **GPT-4o mini** (OpenAI): $0.150 / $0.600, 128K ctx, Q55, value 366.7. **Gemini 2.5 Flash** (Google): $0.300 / $2.50, 1M ctx, Q68, value 226.7. **Gemini 2.5 Flash-Lite** (Google): $0.075 / $0.300, 1M ctx, Q55, value 733.3. **Llama 4 Scout** (Meta): $0.200 / $0.600, 1M ctx, Q60, value 300. **Mistral Small** (Mistral): $0.200 / $0.600, 32K ctx, Q52, value 260. All of these appear in the Compare Prices grid under their respective provider dropdowns. Tick all of them and the grid renders the cross-provider tier comparison in seconds.
When to Pick Each Fast Model
**Claude Haiku 4.5**: pick when traffic volume is high enough that the per-token savings dominate ($1.00 input is hard to beat). **GPT-4o mini**: pick when traffic volume is high enough that the per-token savings dominate ($0.150 input is hard to beat). **Gemini 2.5 Flash**: pick when traffic volume is high enough that the per-token savings dominate ($0.300 input is hard to beat). **Gemini 2.5 Flash-Lite**: pick when traffic volume is high enough that the per-token savings dominate ($0.075 input is hard to beat). **Llama 4 Scout**: pick when traffic volume is high enough that the per-token savings dominate ($0.200 input is hard to beat). **Mistral Small**: pick when traffic volume is high enough that the per-token savings dominate ($0.200 input is hard to beat). The picks aren't mutually exclusive — many production stacks route different traffic types to different Fast models within the same week. For routing pattern guidance, see multi-model routing with quality scores.
Operationalizing the Fast Pick
Once you've shortlisted within the Fast tier in /tools/compare-prices, plug your token volume into /tools/api-cost-estimator for monthly cost projection. A common mistake: assuming Fast models all behave the same on output cost. The grid makes the spread obvious — output costs across the Fast tier in this guide span $0.300 to $5.00 per 1M, a 16.7× spread. Both the price denominator (OpenRouter) and the quality numerator (Arena AI + Artificial Analysis) refresh hourly. So the comparison you screenshot Monday morning is still trustworthy at standup Tuesday morning — but you should re-run it before a quarterly model-routing review. Try the comparison yourself at /tools/compare-prices — it's the fastest way to stack model cost, context, and quality in a single grid.