Claude Opus 4.8 vs Opus 4.7: how to run a real API benchmark

Quick answer: identical pricing, so behavior is the only variable.

Claude Opus 4.8 and Claude Opus 4.7 sit on the exact same rate card here: $4.00 per million input tokens and $20.00 per million output tokens for both model IDs, each 20% below Anthropic's own direct list price of $5.00 input and $25.00 output. Both also carry the same 1M-token context window. None of the usual cost levers, tier, context size, discount, separate the two, which means a benchmark comparing them is really a question about how each one behaves on your prompts, not about which one is cheaper to run. That is a different starting point than most model-versus-model comparisons, where the first thing worth checking is the price gap. Here there isn't one, so the rest of this page treats price as fixed and walks through what is actually worth testing before you commit a workload to claude-opus-4-8, claude-opus-4-7, or a mix of both behind the same routing layer.

The axes worth testing before you pick one.

Point releases inside one model family tend to move on a fairly predictable axis: a newer checkpoint often reasons more before it answers, which shows up as longer, more explanatory output and can help or hurt depending on the task, while an established checkpoint that has already absorbed a lot of production traffic often holds a tighter line on strict formatting instructions. That is a general pattern across model families, not a guarantee about this specific pair, and it is exactly why the test battery above exists instead of a single "which one is smarter" prompt. Run each axis on your own prompts before assuming either model wins across the board.

• Coding correctness: whether the generated function passes your own test suite, not just whether it reads plausibly.
• Strict JSON and tool-schema output: whether a "JSON only" instruction actually returns parseable JSON with no extra prose wrapped around it.
• Long-context recall: whether a fact planted early in a long document survives to the final answer after a lot of context has been read.
• Multi-step reasoning: constraint problems and multi-step derivations where several conditions have to be held at once.
• Multilingual structured output: whether a non-English response still respects a strict schema instead of drifting into free text.
• Tool-use planning: whether a multi-step plan comes back as a clean, executable list of steps or needs cleanup before a script can consume it.

Worked example: what it costs to actually run the comparison.

A battery like the six axes above is cheap to run at flagship rates as long as each task stays short. The table below prices out running all six tasks, five times each for basic statistical stability, against both model IDs at current catalog rates. Token counts are estimates built from typical prompt and response lengths for tasks like these, not a measured result, since your own prompts will run longer or shorter.

Why one test run, or one forum thread, can mislead you.

Single runs carry noise from prompt phrasing, sampling temperature, and time-of-day load. Treat any one benchmark, including this page, as one data point, and rerun the suite on your own workload before you commit a default model.

• Temperature and sampling mean the same prompt can return a different answer on back-to-back calls. A single anecdote about one model "winning" a coding task proves less than it feels like it does; run each task more than once before trusting the result.
• A dated model ID and a rolling alias are not the same test. Pin the exact ID you are comparing, claude-opus-4-8 or claude-opus-4-7, since an alias that auto-updates can quietly change which checkpoint you are calling mid-comparison.
• Judging "better" by response length rewards verbosity, not correctness. A model that writes three paragraphs is not automatically more right than one that answers in three lines; grade against your own test suite or a rubric, not against word count.
• A handful of requests is not a benchmark. Transient latency spikes and one unlucky truncated response can look like a systematic difference when they are just noise.
• Client-side routing or fallback logic can silently substitute a different model than the one you requested. Check the model field on the response itself, not just the one in your request body, before attributing a result to the wrong ID.

Claude Opus 4.8 vs Opus 4.7, side by side.

With price and context tied, this table is mostly useful as a reference for what does not change between the two, plus where a cheaper model in the same family fits if neither Opus tier is worth the spend for a given task.

How to run a benchmark that actually holds up.

None of this requires picking a permanent default. A workable rollout is to run the six-axis battery on your own top three or four prompt types, keep whichever model wins each category as the default for that category, and re-run the comparison the next time either checkpoint changes, since a point release is exactly the kind of update that can shift these tradeoffs again.

• Pin exact model IDs. Compare claude-opus-4-8 against claude-opus-4-7 directly instead of a moving alias, so a provider-side default change never gets misread as a quality difference.
• Run every task at least three to five times per model before drawing a conclusion, and keep temperature low or at zero for tasks where you need repeatable output.
• Score against your own test suite or rubric, not a general impression. A passing unit test or a schema validator gives you a number; "it felt smarter" does not.
• Log finish_reason and completion tokens on every call. A response cut off by max_tokens is a configuration problem, not a finding about which model reasons better.
• Test with your real prompts and your real system prompt, not a generic benchmark question pulled from a blog post. Verbosity and formatting behavior both shift with prompt structure.
• Route both model IDs through one OpenAI-compatible endpoint such as APIsRouter, priced 20% below Anthropic's direct list rate on both models, so a multi-day comparison run does not cost more than the answer is worth.

Test both model IDs from one endpoint.

Both IDs speak the same OpenAI-compatible chat completions format, so the script below is one request sent twice with only the model field changed. Log the model field from the response itself, the finish_reason, and the token usage block for every call, and repeat each prompt a handful of times rather than once before comparing.

from openai import OpenAI
import time

client = OpenAI(
    api_key="sk-...",
    base_url="https://api.apisrouter.com/v1",
)

prompt = "YOUR_TEST_PROMPT"

for model in ("claude-opus-4-8", "claude-opus-4-7"):
    start = time.time()
    r = client.chat.completions.create(
        model=model,
        temperature=0,
        max_tokens=1500,
        messages=[{"role": "user", "content": prompt}],
    )
    elapsed = time.time() - start
    u = r.usage
    print(r.model, r.choices[0].finish_reason, round(elapsed, 2), u.prompt_tokens, u.completion_tokens)

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