Why a Three-Way Grid Beats Pairs for coding LLM picks
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. A two-model comparison answers "which of these wins," but it doesn't catch the third option that quietly dominates both — and that's a common case for coding LLM picks. This guide grids **Claude Opus 4**, **GPT-5**, and **Claude Sonnet 4.7** in /tools/compare-prices so you can see all three side-by-side: input rates, output rates, context windows, and quality scores in stacked columns. The trio was picked because each represents a different point on the quality-vs-price curve — GPT-5 at the budget end, Claude Opus 4 at the quality end, and GPT-5 winning the Value column with 65.6 (quality ÷ input cost). Pair this with flagship/balanced/fast/reasoning LLM tiers, Arena AI leaderboard Elo scores explained, and how LLM quality scores are calculated.
Building the Grid: Provider Dropdowns and Picks
Open /tools/compare-prices. Tick **Claude Opus 4** in the **Anthropic** dropdown, **GPT-5** in the **OpenAI** dropdown, and **Claude Sonnet 4.7** in the **Anthropic** dropdown. The grid stacks them with input cost / output cost / context / quality in one row each. Claude Opus 4: $15.00 in, $75.00 out, 200K ctx, Q85. GPT-5: $1.25 in, $10.00 out, 200K ctx, Q82. Claude Sonnet 4.7: $3.00 in, $15.00 out, 200K ctx, Q80. The model ID column (visible on hover) is the string you paste into your SDK call.
Reading the Cost Spread
Input cost spans $1.25 to $15.00 — a 12.0× spread. Output cost matters more than input when reply length exceeds prompt length (typical for content generation, agents, code). Output-to-input ratios: Claude Opus 4 5.0×, GPT-5 8.0×, Claude Sonnet 4.7 5.0×. The model with the lowest output ratio tends to be the cheapest for generation-heavy workloads, regardless of input rate. For a workload mix calculator that bakes in your specific in/out ratio, run the same models through /tools/api-cost-estimator.
Quality and Value Tradeoffs
**Claude Opus 4** leads on quality (85). **GPT-5** leads on raw cost ($1.25/1M input). **GPT-5** leads on the Value column (65.6) — meaning the highest quality-adjusted return per dollar. In practice, this means: when accuracy is non-negotiable, pay for Claude Opus 4; when budget is the binding constraint and quality just needs to clear a floor, ship GPT-5; when you want the best default for production routing, GPT-5 is the answer. For the methodology behind that Value formula, see Value column vs tokens per dollar and quality per dollar LLM ranking 2026.
Workflow: From Grid to Production Decision
Once the trio is on screen, the workflow is: (1) eliminate any model whose quality is below the floor your workload tolerates — score < 50 for customer-facing, < 65 for reasoning-heavy. (2) of the survivors, compare Value column rankings. (3) for the top one or two, estimate monthly bill via /tools/api-cost-estimator using your expected token volume. (4) ship a 1-week A/B with Claude Opus 4 as the gold-standard control and your Value pick as the candidate. The Compare Prices grid is the first step in that funnel — it eliminates the wrong picks fast. The grid pulls prices live from OpenRouter and quality from a blended Arena AI + Artificial Analysis pipeline — both refresh on a 60-minute incremental cache, so the comparison reflects current rates not a baked-in snapshot. Try the comparison yourself at /tools/compare-prices — it's the fastest way to stack model cost, context, and quality in a single grid.