Claude Sonnet 5 vs Opus 4.8 (2026): 60% Cheaper on Paper

TL;DR Anthropic shipped Claude Sonnet 5 on June 30, 2026 at introductory pricing of $2/$10 per million tokens, 60% under Opus 4.8’s $5/$25 (the standard rate after August 31 is $3/$15, still 40% under). On capability, Opus 4.8 keeps the two rows that matter for hard work: SWE-bench Pro 69.2% vs 63.2% and a ~6.6-point no-tools reasoning lead. Two things quietly narrow the price gap: a new tokenizer that counts about 30% more tokens than Sonnet 4.6, and adaptive thinking on by default, which Artificial Analysis pegs at roughly 15% higher cost per agentic task than Opus 4.8. The sticker says 60% off. The bill says “it depends on your workload.” Below: the exact math, the benchmark table, two real monthly bills, and a routing pattern that uses both.

Claude Sonnet 5 lists 60% below Opus 4.8, but adaptive thinking and a new tokenizer mean an output-heavy agentic workload can cost the same or more. The discount is real for bounded output and imaginary for long agent runs.

TL;DR: Which One Should You Pick?

For most teams the answer is “Sonnet 5 as the default, Opus 4.8 for the hard tail.” Here is the one-line verdict by scenario.

The rest of this piece is the evidence behind that table, plus a 10-line way to A/B both on your own workload before you commit.

Quick Specs Comparison

Both models share the same nominal 1M context window and 128K max output. The differences are price, the tokenizer, and the default thinking behavior.

The intro list prices ($2/$10 and $5/$25) match the ofox model pages for anthropic/claude-sonnet-5 and anthropic/claude-opus-4.8 as of July 1, 2026; the introductory-versus-standard split and the August 31 cutoff come from Anthropic’s official pricing docs. Note the standard output rate: after the intro window Sonnet 5 lands at $15/M against Opus 4.8’s $25/M, so the output gap narrows from 60% to 40%.

The Price Gap Is Real. Here Is the Exact Math.

On per-token rates, Sonnet 5 is genuinely cheaper, and it is cheaper on every line: input, output, and cached reads.

During the introductory window (through August 31, 2026), Sonnet 5 is $2/$10 against Opus 4.8’s $5/$25. That is 60% off input and 60% off output. After August 31 the standard rate kicks in at $3/$15, which is 40% off both lines. Cached input reads are $0.2/M versus $0.5/M, a 60% cut that holds regardless of the intro window and matters a lot for prompt-cache-heavy production traffic.

So if your workload is dominated by input tokens and produces short, bounded output, Sonnet 5 does exactly what the headline promises. The place the story gets complicated is anything that generates a lot of output, which is most agentic work.

One line in the specs table deserves more weight than it usually gets: cached input. Sonnet 5 reads cached input at $0.2/M against Opus 4.8’s $0.5/M. If your prompts carry a large stable prefix (a system prompt, a tool schema, a retrieved document set that repeats across calls), prompt caching is where the real money is, and Sonnet 5’s cache read is 60% cheaper regardless of the introductory window. A production RAG endpoint that caches a 20K-token prefix across thousands of calls pays for that prefix at $0.2/M on Sonnet 5 instead of $0.5/M on Opus 4.8. The catch is the write side: Sonnet 5 writes cache at $2.5/M (5 minute) or $4/M (1 hour) versus Opus 4.8’s $6.25 and $10, so caching pays off faster on Sonnet 5, but only if your hit rate is high enough to amortize the write. Below roughly a 1:1 to 1.5:1 read-to-write ratio, caching costs more than it saves on either model.

The New Tokenizer, and Who It Actually Affects

Sonnet 5 ships a new tokenizer. This is the part of the launch most likely to surprise you on the bill, and it is also the part most often misread.

The verified facts, straight from Anthropic’s “What’s new in Sonnet 5” docs: the same input text produces approximately 30% more tokens on Sonnet 5 than on Sonnet 4.6. Community measurements put the spread at 1.0 to 1.35x depending on content. It is not an API change (requests, responses, and streaming keep the same shape), but it moves everything you count in tokens:

Here is the misread to avoid: this 30% is measured against Sonnet 4.6, not against Opus 4.8. Anthropic introduced this style of tokenizer change earlier, around Opus 4.7, so Opus 4.8 already runs a comparable prior-generation tokenizer. For the same text, Sonnet 5 and Opus 4.8 land in roughly the same token ballpark. The tokenizer bites hardest when you are migrating from Sonnet 4.6 to Sonnet 5 and reusing old token budgets, not when you are choosing between Sonnet 5 and Opus 4.8.

The practical takeaway: if you are coming from Sonnet 4.6, recount your prompts with the token-counting endpoint and revisit any max_tokens sized close to your expected output before you trust the “same $3/$15 price” framing. Same per-token price, more tokens, higher bill. Our Claude Code token optimization guide covers how to claw that back with caching and prompt trimming.

Coding Benchmark: SWE-bench Pro and the Real Gap

Coding benchmarks are noisy, but SWE-bench Pro is the one worth arguing about because it runs against real GitHub issues end to end. Here is where the two land, with Sonnet 4.6 for reference.

The SWE-bench Pro and GDPval-AA v2 figures were compiled by MarkTechPost from Anthropic’s launch materials, June 30, 2026; the ~6.6-point no-tools reasoning gap comes from Anthropic’s System Card (via digitalapplied.com and codingfleet.com), not MarkTechPost. Treat leaderboard-style scores as a snapshot, and see Anthropic’s Transparency Hub for the per-benchmark source. Two things in that table decide most routing calls.

Opus 4.8 keeps the 6-point SWE-bench Pro lead. Sonnet 5 at 63.2% is a real jump over Sonnet 4.6’s 58.1%, but Opus 4.8’s 69.2% is still the number to beat for hard, multi-file agentic issues. Six points on SWE-bench Pro is the difference between “closes the issue on the first run” and “closes it after a retry,” and on long agent loops that compounds into token spend. If your work lives at that ceiling, the cheaper model is not actually cheaper once you count retries.

Sonnet 5 wins knowledge work by a hair. On the GDPval-AA v2 economic-work leaderboard, Sonnet 5 edges Opus 4.8 by three Elo points (1,618 to 1,615). That is inside the noise, but the point stands: for general professional tasks that are not the hardest coding, Sonnet 5 is at parity with a model that costs more than twice as much. Anthropic’s own framing is that Sonnet 5’s higher-effort mode can match Opus 4.8 on some tasks while offering a wider cost-performance range.

It helps to know what these two benchmarks actually measure before you weight them. SWE-bench Pro runs models against real, unsolved GitHub issues end to end: the model reads the repo, writes a patch, and the patch either passes the project’s hidden test suite or it does not. There is no partial credit, which is why the absolute numbers look low next to multiple-choice evals. GDPval-AA v2 is a different shape. It scores models on real economic knowledge work (drafting, analysis, structured reasoning) as an Elo rating against other models, so a 3-point edge is a coin flip and a 100-point edge is decisive. Read together, the tables say one thing clearly: Opus 4.8 is meaningfully better at closing hard code issues, and Sonnet 5 is at parity for general professional output. That is the whole case for routing rather than picking a single winner.

Pricing Math: Two Real Monthly Bills

Sticker price is one number. The bill is another. Here are two workloads that produce opposite conclusions, with the assumptions stated so you can swap in your own.

Scenario A, high-volume bounded output (support bot, classification, extraction). Assume 300M input tokens/month with half served from cache, and 30M output tokens/month.

Here the discount is exactly what the headline says. Bounded output means the cheaper per-token rates flow straight to the bottom line.

Scenario B, agentic coding (long multi-step runs, thinking on). Assume 5 developers, 25 tasks/day each, 20 workdays (2,500 tasks/month). Per task: 60K input on both. Output: 12K on Opus 4.8, but about 30K on Sonnet 5 because adaptive thinking is on by default and it reasons more per task.

At standard pricing, an output-heavy agentic workload can cost slightly more on Sonnet 5 than on Opus 4.8, because the extra thinking tokens land on the output line. My illustrative model shows +5%; Artificial Analysis’s independent cost-to-run estimate put it closer to +15% ($2.29 per task versus Opus, snapshot late June 2026). The exact number depends on how much your tasks think. The direction does not: the sticker discount does not survive contact with long agent runs. This is the single most important thing to internalize before you migrate an agent fleet.

When to Pick Claude Sonnet 5

Pick anthropic/claude-sonnet-5 when output is bounded and volume is high. Concretely:

• Classification, extraction, routing, moderation. Short outputs, huge input volume, often cache-heavy. Sonnet 5’s $2/$10 and $0.2/M cached reads cut these bills 40 to 60%.
• RAG answers and summarization. The retrieval does the heavy lifting; the model writes a bounded response. Capability is plenty, price wins.
• Routine coding. Single-file edits, boilerplate, test scaffolding, code review comments. Sonnet 5’s 63.2% SWE-bench Pro is more than enough for work that is not at the frontier.
• Chat and assistant surfaces. Interactive turns are short; Sonnet 5’s speed and price fit better than an Opus-class model.

When to Pick Claude Opus 4.8

Pick anthropic/claude-opus-4.8 when the task is hard enough that a wrong first answer costs more than the price difference:

• Frontier agentic coding. The 6-point SWE-bench Pro lead is the difference between one run and a retry loop. On hard multi-file issues, Opus 4.8 finishes in fewer turns, and fewer turns is fewer tokens. We cover the model in depth in our Opus 4.8 release review.
• Long-horizon reasoning without tools. The ~6.6-point no-tools reasoning gap shows up as “the plan holds together” on complex multi-step problems.
• Output-heavy agent loops where you measured Sonnet 5 and it came out even or higher. If your per-task cost is the same either way, take the model with the higher benchmark.

When Not to Pick Either (and What to Do Instead)

The trap is treating this as a binary swap. Most production workloads are mixed: a lot of cheap bounded calls plus a small tail of genuinely hard tasks. Forcing all of it onto one model overpays on the easy 80% or underperforms on the hard 20%.

The fix is routing. Send bounded, high-volume work to Sonnet 5 and the hard tail to Opus 4.8, behind one endpoint so switching a model is a one-string change, not a re-integration. That pattern, and how to pick the routing signal, is in our Claude Code hybrid routing pattern writeup. Through ofox both models sit on the same OpenAI-compatible API, so a router is a dictionary lookup, not a second SDK.

The hard part of routing is not the plumbing, it is the signal: how do you decide, per request, whether a task is hard before you run it? Three signals work in practice. Input length is the cheapest proxy, since requests over some token threshold tend to be the multi-file, high-context tasks that reward Opus 4.8. A task-type tag from your own application (classification versus open-ended agentic work) is more accurate if you already have it. And a confidence check works as a fallback: run Sonnet 5 first, and escalate to Opus 4.8 only when the cheaper model’s output fails a validation step. The escalation pattern keeps the Opus share small, which is the whole point, since Opus is the expensive tier you want to touch as rarely as the work allows.

flowchart TD
    A[Incoming request] --> B{Bounded output?<br/>classification, RAG, chat}
    B -->|Yes| C[anthropic/claude-sonnet-5]
    B -->|No| D{Frontier coding or<br/>long-horizon reasoning?}
    D -->|Yes| E[anthropic/claude-opus-4.8]
    D -->|No, measure it| F[A/B both, pick lower per-task cost]

Try Both via ofox: A/B in 10 Lines

The honest way to settle this is to run both on your own workload and read the token counts. ofox exposes both models on one OpenAI-compatible endpoint (https://api.ofox.ai/v1), so the only thing that changes between runs is the model ID string. One gotcha: Sonnet 5 rejects non-default temperature, top_p, and top_k with a 400 error, so leave sampling parameters at their defaults (the examples below do).

Python: A/B both models in one loop

from openai import OpenAI

client = OpenAI(base_url="https://api.ofox.ai/v1", api_key="YOUR_OFOX_KEY")

prompt = "Refactor this function to remove the nested loop: ..."
for model in ["anthropic/claude-sonnet-5", "anthropic/claude-opus-4.8"]:
    r = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
    )
    u = r.usage
    print(model, u.prompt_tokens, u.completion_tokens)

Read the completion_tokens for each. That column, times the output rate, is where the “cheaper” model can quietly stop being cheaper.

Node: same shape

import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.ofox.ai/v1", apiKey: process.env.OFOX_KEY });

const prompt = "Refactor this function to remove the nested loop: ...";
for (const model of ["anthropic/claude-sonnet-5", "anthropic/claude-opus-4.8"]) {
  const r = await client.chat.completions.create({
    model,
    messages: [{ role: "user", content: prompt }],
  });
  console.log(model, r.usage.prompt_tokens, r.usage.completion_tokens);
}

Run this on 20 or 30 representative tasks, sum the input and output tokens per model, and multiply by the rates in the specs table. That number beats any benchmark for deciding which model to route where. For the full pricing breakdown across the Claude line, see our Claude API pricing guide.

Migration Gotchas: What Breaks Moving to Sonnet 5

Sonnet 5 is a drop-in replacement for Sonnet 4.6 in shape, but three behavior changes will return 400 errors if your old code assumes 4.6 defaults. These also apply relative to Opus 4.8 code in most cases.

The max_tokens one is the sneaky failure. If you sized output budgets tightly against Sonnet 4.6, the same generation on Sonnet 5 produces more tokens for the same text and can hit the ceiling mid-answer. Bump the budget or you will ship truncated responses. There is also a new safeguard to know about: Sonnet 5 is the first Sonnet-tier model with real-time cybersecurity refusals, which return as a successful HTTP 200 with stop_reason: "refusal" rather than an error, so handle that stop reason explicitly.

Adaptive thinking is the change most likely to move your bill, and it comes with a dial. In place of the old budget_tokens knob, Sonnet 5 exposes an effort parameter (low, medium, high) that trades reasoning depth against token spend. If you migrated an Opus 4.8 workload expecting Sonnet 5 to be cheaper and the bill came in flat, the first thing to try is lowering effort on the calls that do not need deep reasoning. High effort on a classification call is pure waste, and it is where a lot of the surprise cost in Scenario B comes from. Set effort deliberately per route rather than leaving every call at the default.

The clean migration test is not the benchmark score. It is the completion_tokens column: run both models on your real tasks, and let the token count, not the price sheet, decide the routing.

FAQ

Is Claude Sonnet 5 better than Opus 4.8? Not across the board. Opus 4.8 leads SWE-bench Pro (69.2% vs 63.2%) and no-tools reasoning (about 6.6 points). Sonnet 5 edges knowledge work (GDPval-AA v2: 1,618 vs 1,615) and wins on price. Sonnet 5 is the better default; Opus 4.8 earns its premium on the hardest tasks.

How much cheaper is Claude Sonnet 5 than Opus 4.8? 60% at introductory pricing ($2/$10 through August 31, 2026), 40% at the standard $3/$15 rate afterward. Cached input is 60% cheaper too ($0.2/M vs $0.5/M).

Does Claude Sonnet 5 use a new tokenizer? Yes, and it produces about 30% more tokens for the same text than Sonnet 4.6. It is not an API change, but recount prompts and revisit max_tokens if you are migrating from 4.6.

Why does Claude Sonnet 5 cost more per task than the price suggests? Adaptive thinking is on by default, so it emits more output tokens per task. Artificial Analysis estimated roughly $2.29 per task, about 15% above Opus 4.8 on their agentic evaluation.

Is Claude Sonnet 5 good for coding? Yes for most coding (63.2% SWE-bench Pro, up from 58.1% on Sonnet 4.6). Route the hardest agentic issues to Opus 4.8.

Should I switch from Opus 4.8 to Sonnet 5? Switch the high-volume bounded-output part and cut that bill 40 to 60%. Keep Opus 4.8 for the hard tail. Route, do not replace.

What is the context window of Claude Sonnet 5? 1M tokens, 128K max output. The new tokenizer means that window holds less actual text than the same window on Sonnet 4.6.

Can I set temperature on Claude Sonnet 5? No. Non-default temperature, top_p, or top_k returns a 400 error. Remove them and steer via the system prompt.

Sources Checked for This Refresh

• Anthropic, “What’s new in Claude Sonnet 5” docs (tokenizer, behavior changes, pricing), verified July 1, 2026: https://platform.claude.com/docs/en/about-claude/models/whats-new-sonnet-5
• Anthropic, “Introducing Claude Sonnet 5” launch post, June 30, 2026: https://www.anthropic.com/news/claude-sonnet-5
• Anthropic Transparency Hub (per-benchmark source): https://www.anthropic.com/transparency
• MarkTechPost benchmark compilation (SWE-bench Pro, GDPval-AA v2 only), June 30, 2026
• Anthropic System Card via digitalapplied.com and codingfleet.com (no-tools reasoning gap, ~6.6 points)
• Artificial Analysis cost-to-run estimate ($2.29/task), snapshot late June 2026
• ofox model pages for anthropic/claude-sonnet-5 and anthropic/claude-opus-4.8 (intro list pricing $2/$10 and $5/$25, context window), verified July 1, 2026; intro/standard tiering and the August 31 cutoff per Anthropic’s pricing docs