Why a Side-by-Side Comparison of Claude Opus 4 and GPT-5 Matters
The Compare Prices tool is the fastest way to put a shortlist of LLMs in a single grid — input cost, output cost, context window, and quality score in stacked columns you can scan vertically. Provider dropdowns let you mix models across Anthropic, OpenAI, Google, Meta, DeepSeek, Mistral, and xAI without leaving the page. For Claude Opus 4 vs GPT-5, the side-by-side framing matters because both models sit near the same workload niche — one of you ships the wrong pick and the bill (or quality regression) is months of pain. Claude Opus 4 runs $15.00 / $75.00 per 1M tokens with a 200K context and a blended quality score of 85. GPT-5 runs $1.25 / $10.00 per 1M with a 200K context and quality 82. Sticker prices don't tell the whole story — the Value column (quality ÷ input cost) gives Claude Opus 4 a 5.7 and GPT-5 a 65.6, which is the number you actually want to optimize when shipping production traffic. 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.
Claude Opus 4 in the Compare Prices Grid
In the Compare Prices view, click the **Anthropic** dropdown and check **Claude Opus 4**. The row shows input at $15.00/1M, output at $75.00/1M, 200K context, and the blended quality badge at 85. Claude Opus 4 sits in TokenRate's **flagship** tier — flagship tier is for frontier-quality use cases where the per-token price is a rounding error against the value of the output. The output-to-input ratio of 5.0x is worth flagging because generation-heavy workloads (long summaries, code, structured output) compound that multiplier across every reply. For a single-shot classifier the input price dominates; for an agent generating ~10× the tokens it reads, you're effectively paying $75.00 per 1M.
GPT-5 in the Compare Prices Grid
Add **GPT-5** from the **OpenAI** dropdown. The grid lists input $1.25/1M, output $10.00/1M, 200K context, quality 82, tier **flagship**. flagship tier is for frontier-quality use cases where the per-token price is a rounding error against the value of the output. Compared to Claude Opus 4, GPT-5 is cheaper on input (by 92%) and lower on quality (by 3 points). Context-window-wise, GPT-5 has a tighter window — relevant if you're feeding long documents.
Where Claude Opus 4 Wins and Where GPT-5 Wins
**GPT-5 wins on raw cost** ($1.25 vs $15.00 input — about 12.0× cheaper) — so it's the right pick for high-volume features where the model is fungible across the chosen tier. **Claude Opus 4 wins on quality** (85 vs 82) — important when you're routing reasoning-heavy or accuracy-critical traffic. **GPT-5 wins on Value** (65.6 vs 5.7) — meaning per dollar of input you get more quality-adjusted output, which is what the Value column optimizes for. Both have 200K context — neither wins on document length.
Decision Heuristics and What to Do Next
Three heuristics: (1) if your monthly bill on the pricier option exceeds 4× your engineering team's comfort and the cheaper option's quality is within 5 points — ship the cheaper one and pocket the savings. (2) if the workload is reasoning-heavy or customer-facing premium, pay the quality premium even when the Value column says otherwise. (3) hedge: route 80–90% of traffic to the cheaper model and fall back to the pricier one for tail-quality cases. The fallback router pattern works because output-cost only matters when you actually call it. For the routing implementation, see multi-model routing with quality scores. Pricing is pulled live from OpenRouter's models endpoint and revalidated every 60 minutes via Next.js's incremental cache, so the grid you see is at most an hour stale. Quality scores blend Arena AI Elo with Artificial Analysis intelligence-index data on the same cadence.