Claude Opus 5 vs GPT-5.6 Sol (2026): Benchmarks + Real Bill
TL;DR. Claude Opus 5 and GPT-5.6 Sol are two frontier models priced within a rounding error of each other, and the "cheaper" one depends on where you buy. On vendor list price they match on input ($5/M) and cache ($0.50/M), and differ only on output: $25/M on Opus 5 versus $30/M on Sol, so on list Opus 5 is ~17% cheaper. On ofox as of July 27, 2026, Sol is on a -20% promo ($4/$24/$0.40) while Opus 5 sits at list, which flips it: on ofox, Sol is currently cheaper on every line. On the one third-party benchmark that scores both on the same harness, the Artificial Analysis Intelligence Index v4.1, Opus 5 leads by two points (61 vs 59 at max effort). Every other launch number is vendor-reported on a different harness and cannot be lined up. Sol streams tokens ~1.4x faster (75.9 vs 52.8 tok/s); Opus 5 reaches first token ~1.9x faster (69.7s vs 129.5s). Both live on one ofox key, so the honest way to decide is a one-line A/B on your own tasks. This post gives you the numbers that are real, flags the ones that aren't, and hands you the harness.
TL;DR: Which One Should You Pick?
| Your job | Pick | Why |
|---|---|---|
| Cheapest on ofox right now | Sol | ofox runs Sol at -20% ($4/$24/$0.40); cheaper on every line while the promo holds |
| Cheapest on vendor list price | Opus 5 | Output $25/M vs $30/M; input and cache identical |
| Highest score on a neutral composite | Opus 5 | AA Intelligence Index 61 vs 59 at max effort |
| Latency-sensitive interactive turns | Opus 5 | ~69.7s to first token vs ~129.5s on the same test |
| Streaming a long answer as fast as possible | Sol | ~75.9 output tok/s vs ~52.8 |
| You already run the OpenAI Responses API and want Pro/Ultra | Sol | Heavy-compute modes are Sol-only |
| Computer use and desktop automation | Opus 5 | Anthropic's OSWorld/AutomationBench framing (vendor, unverified) |
One sentence: Opus 5 wins the one comparable benchmark and starts faster; Sol streams faster, owns the heavy-compute ceiling, and is the cheaper model on ofox while its promo lasts. These two are close enough that the tie-breaker is your own workload, so keep both on one key and A/B them.
Quick Specs Comparison
Both models are current flagships, both went live within three weeks of each other (Sol at GA on July 9, Opus 5 on July 24). The table below lists vendor list price and the ofox price separately, because they differ: ofox is running Sol on a 20% promo. All figures verified against each model's ofox page on July 27, 2026.
| Spec | Claude Opus 5 | GPT-5.6 Sol |
|---|---|---|
| ofox model ID | anthropic/claude-opus-5 |
openai/gpt-5.6-sol |
| Input (list) | $5.00 / M | $5.00 / M |
| Output (list) | $25.00 / M | $30.00 / M |
| Cached input (list) | $0.50 / M | $0.50 / M |
| ofox price (Jul 27) | $5 / $25 / $0.50 (list, no promo) | $4 / $24 / $0.40 (-20% promo) |
| Context window | 1,000,000 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | 128,000 tokens |
| Reasoning control | Adaptive thinking + effort low–max
|
reasoning_effort none–xhigh (chat); Responses API for max
|
| Heavy-compute mode | Fast Mode (speed), no parallel-subagent tier | Pro / Ultra (Sol only, Responses API) |
| Native API | Anthropic Messages | OpenAI Responses / chat |
On paper the two nearly match: same context, same output cap, and the same $5 list input. The two spots that actually separate them are price (identical on list except output, but currently reshuffled by ofox's Sol promo) and which native API you reach for reasoning control. The pricing section below works both prices through a real bill.
The One Number You Can Actually Compare
Here is the trap in every GPT-5.6-vs-Opus-5 post you will read this week: they build a big table with Terminal-Bench in one row and CursorBench in the next and a SWE-Bench number below that, as if those figures came off the same ruler. They did not. Anthropic ran Opus 5 on Anthropic's harness. OpenAI ran Sol on OpenAI's. A benchmark name is not a shared measurement.
The one place both models are scored by the same third party, on the same tasks, with the same grader, is the Artificial Analysis Intelligence Index v4.1. That index is a weighted composite of nine evaluations, including GDPval-AA v2, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, and AA-LCR, weighted roughly 34% toward agents, 24% coding, 24% scientific reasoning, and 18% general intelligence. Because Artificial Analysis runs both models itself, on its own harness, its composite is the closest thing to an apples-to-apples read that exists right now, and the eval mix leans toward exactly the agentic and coding work most people are choosing between these two for.
| Artificial Analysis Intelligence Index v4.1 | Claude Opus 5 | GPT-5.6 Sol |
|---|---|---|
| Intelligence Index, max effort | 61 | 59 |
Source: Artificial Analysis comparison page, read July 27, 2026. Opus 5 is scored in Adaptive Reasoning at max effort; Sol at max. AA is a rolling benchmark, so the exact figures can shift, re-read the page on publish day.
Opus 5 leads by two points at max effort. That is a real, reproducible, same-harness result, and it is the strongest single fact in this comparison. It is also small. For context, the same index puts Claude Fable 5 at 60 (max), which means AA's composite rates Opus 5 a hair above Anthropic's own larger flagship, and rates Sol a hair below it. Read that carefully before you over-invest in it. A two-point gap on a nine-eval composite is not a knockout. It tells you Opus 5 is at least Sol's equal on a broad neutral test, and probably a shade better, but it does not tell you which one is better at your refactor, your migration, or your agent loop. That is what the A/B at the end is for.
Why the Launch Benchmarks Don't Line Up
Both vendors published launch numbers. They are worth reading, as long as you read them as two separate marketing decks and never as a shared table. Split by source:
Anthropic's Opus 5 figures (Anthropic internal, unverified, from the Opus 5 announcement):
| Benchmark | Anthropic's claim |
|---|---|
| Frontier-Bench v0.1 | Surpasses all other models; more than doubles Opus 4.8 |
| CursorBench 3.2 | Within 0.5% of Fable 5's peak, at half the cost |
| ARC-AGI-3 | Roughly 3x the next-best model |
| OSWorld 2.0 (computer use) | Beats Fable 5 at one-third the cost |
| GDPval-AA (economic work) | State of the art |
OpenAI's GPT-5.6 Sol figures (OpenAI's own harness, from the GA launch):
| Benchmark | OpenAI's claim |
|---|---|
| Terminal-Bench 2.1 | 88.8% base, 91.9% in Ultra mode |
| Agents' Last Exam | 53.6 (medium beats Fable 5 by 11.4 pts at ~1/4 cost) |
| SWE-Bench Pro | 64.6% |
You cannot subtract one table from the other. Notice what happens if you try: OpenAI's own SWE-Bench Pro line has Sol at 64.6%, while Anthropic reports Fable 5 at 80% on the same-named benchmark, which would suggest the Claude family is well ahead on that one. But those two figures also come from different harness runs, so even that comparison is soft. The honest position is that the launch decks establish each model is frontier-class in its vendor's own tests, and nothing more. Two more points worth keeping straight, because loose paraphrases are already circulating:
- 91.9% is Ultra, not base. The Terminal-Bench headline that gets quoted as "GPT-5.6 hits 91.9%" is Sol running the heavy-compute Ultra mode, a parallel-subagent architecture that is not the default and costs a lot more per task. Base Sol is 88.8%. The 3.1-point Ultra gain rarely pays for itself outside the hardest agent problems.
- The "half the price of Fable 5" line is Anthropic's, and it is about Opus 5, not Sol. Opus 5's pitch is near-Fable-5 intelligence at half the Fable price. Do not attach it to GPT-5.6.
For a fuller walkthrough of how the Claude flagship tier actually lands on coding tasks, the Fable 5 vs Opus 4.8 vs GPT-5.5 SWE-Bench breakdown splits the harnesses the same way this post does.
Pricing Math: List Price Says Opus 5, ofox Currently Flips It
There are two prices here and they disagree, so keep them apart.
Vendor list price. Anthropic lists Opus 5 at $5 input / $25 output / $0.50 cached per million tokens. OpenAI lists Sol at $5 / $30 / $0.50. Input and cache match exactly, so on list pricing the only line that differs is output, and the whole dollar gap is your output volume at a flat $5/M. On list, Opus 5 is the cheaper model, by 17% on output and nothing else. Artificial Analysis, blending at its own cache/input/output profile, agrees: about $3.85 per 1M for Opus 5 versus $4.35 for Sol.
ofox price today. This is where it flips. As of July 27, 2026, ofox runs GPT-5.6 Sol at a 20% discount, $4 input / $24 output / $0.40 cached, against its $5/$30/$0.50 list. Opus 5 sits at list, $5/$25/$0.50, no promo. So on ofox right now, Sol is the cheaper model on every single line: input $4 < $5, output $24 < $25, cache $0.40 < $0.50. The list-price verdict inverts. Promos move, so re-check both model pages the day you commit a route.
Put real ofox numbers on it. A coding-agent fleet at 100,000 requests a day, 4K input and 2K output per request:
| Line item | Opus 5 ($5/$25) | Sol -20% ($4/$24) |
|---|---|---|
| Input: 4K × 100K/day | $2,000/day | $1,600/day |
| Output: 2K × 100K/day | $5,000/day | $4,800/day |
| Daily total (no cache) | $7,000 | $6,400 |
| Monthly (×30) | ~$210,000 | ~$192,000 |
| Difference | -$18,000/mo on Sol |
Turn on a realistic 70% input cache hit rate and the ordering holds, just tighter. Opus 5's effective input rate drops to about $1.85/M and Sol's to about $1.48/M, landing Opus 5 near $172,000/month and Sol near $162,000, roughly $10K/month apart. The gap narrows under caching because Sol's cheaper input matters less once most input is already cached, but on ofox Sol stays ahead as long as the promo is live.
So the cost answer has a shelf life. On vendor list price Opus 5 wins; on ofox as of late July, Sol's promo makes it cheaper outright. If you buy through ofox, treat price as a point for Sol until the discount ends, and let the AA index lead, the latency split, and the API shape be what actually separate the two.
Two adders the base rate hides, and they do not cancel evenly. A web-search tool call is billed per request, and it is not equal: on ofox, Sol charges $0.035 per search versus $0.015 for Opus 5, so search-heavy agents tilt back toward Opus 5. And the speed premiums are separate line items: Opus 5's Fast Mode runs the same model at up to 2.5x output speed for double the base rate, and Sol's Pro and Ultra modes cost more tokens per task. Model those into your own numbers rather than the base table.
Speed and Latency: They Win Different Races
Cost is a near-tie that depends on where you buy. Speed is a genuine split, and which model is "faster" depends entirely on what you are measuring. Artificial Analysis benchmarked both under load (read July 27, 2026; these are rolling figures):
| Metric (AA, max effort) | Claude Opus 5 | GPT-5.6 Sol |
|---|---|---|
| Output speed | 52.8 tok/s | 75.9 tok/s |
| Time to first token | 69.7 s | 129.5 s |
Sol streams about 1.4x faster once it is producing tokens, so for a long generated answer where the user watches it stream, Sol finishes sooner. Opus 5 reaches the first token roughly 1.9x faster, so for short interactive turns, tool-calling loops, and anything where perceived responsiveness matters, Opus 5 feels quicker. (Both time-to-first-token numbers are large because these are reasoning models under a heavy-effort test; the ordering, not the absolute seconds, is the point.) If you run an interactive coding assistant, low first-token latency usually beats raw throughput, which tilts toward Opus 5. If you run batch generation of long documents, throughput wins, which tilts toward Sol.
The API Reality: What Actually Breaks When You Switch
The benchmarks are a near-tie; the API shapes are not. This is where the two models feel genuinely different, and it is the part most comparison posts skip. If you are migrating a real codebase, these are the things that will actually cost you an afternoon.
Reasoning control lives in different places. Opus 5 uses Anthropic's model: adaptive thinking is on by default, and you steer depth with an effort ladder from low to max (defaulting to high). Sol uses OpenAI's reasoning_effort parameter, which accepts none through xhigh on /v1/chat/completions; OpenAI routes the Pro and Ultra modes, and recommends max effort, through the Responses API. So dialing Opus 5 to maximum reasoning is a one-word change, while Sol's heaviest modes generally mean moving to a second endpoint.
Opus 5 turns thinking on by default; that can truncate you. On Opus 4.8 a request with no thinking field ran without thinking. On Opus 5 the same request reasons, and because max_tokens caps thinking plus visible output together, a workload tuned for silent 4.8 can truncate. Set thinking: {"type": "disabled"} to restore the old behavior, or raise max_tokens. One catch that surprises people: on the Anthropic API, disabling thinking is only allowed at effort high or below, so disabled plus xhigh/max returns a 400.
Sol 404s on the bare ID. On ofox, openai/gpt-5.6 does not resolve; you must pass openai/gpt-5.6-sol (or -terra/-luna). If you A/B by string-swapping the model field, use the explicit tier or you will spend ten minutes debugging a not-found error. Our GPT-5.6 tier guide covers the full Sol/Terra/Luna split if you want the cheaper tiers in the same routing table.
For the complete Opus 5 migration checklist, including the cache-minimum drop to 512 tokens and the sampling-param rejections that carry over from 4.8, the Opus 5 API guide walks through every change.
When to Pick Claude Opus 5
Reach for Opus 5 when:
- You buy at vendor list price and output dominates your bill. Agentic coding, long code generation, verbose tool-use loops. On list, the 17% output discount is real money at scale and predictable to model. (On ofox, note Sol's promo currently undercuts this; see the pricing section.)
- You want the highest neutral score. Opus 5 leads the AA Intelligence Index 61 vs 59 at max effort. It is a two-point lead, but it is the only same-harness lead anyone has.
- First-token latency matters. Interactive assistants and tool-calling agents feel snappier at ~69.7s TTFT versus ~129.5s.
- You are on Claude Code or the Anthropic protocol already. Opus 5 is a drop-in model-string change from 4.8, same $5/$25 list pricing, and it slots into
anthropic/claude-opus-5on ofox with no new SDK. - Computer use and desktop automation are in scope. Anthropic's OSWorld 2.0 and AutomationBench framing is the strongest part of its deck (still vendor-reported, so verify).
When to Pick GPT-5.6 Sol
Reach for Sol when:
- You buy on ofox and want the lowest bill right now. While ofox's -20% promo holds, Sol is cheaper than Opus 5 on every price line ($4/$24/$0.40). Re-check before you commit, since promos expire.
- You stream long answers and throughput is the bottleneck. 75.9 tok/s versus 52.8 is a 1.4x edge on sustained generation.
- You need Pro or Ultra heavy-compute. The parallel-subagent Ultra mode that posted 91.9% on Terminal-Bench has no Opus 5 equivalent. If your hardest agent tasks genuinely benefit from that mode, it lives only on Sol.
- Your stack is already OpenAI-native. If you are deep in the Responses API, tool schemas, and OpenAI's ecosystem, Sol is the zero-friction upgrade and the
maxeffort path is already wired. - You want tier flexibility on one family. Sol, Terra, and Luna share a generation, so you can drop to Terra at half the price for bulk traffic and keep Sol for the hard tail, all on the same key.
When NOT to Pick Either
Some workloads should not be paying flagship rates at all. If your task is high-volume classification, routing, short chat glue, or anything where a bounded, well-specified prompt gets a short answer, both of these models are overkill and overpriced. Send that traffic to a cost tier: on ofox, GPT-5.6 Luna runs about $0.80/$4.80 per M and Claude Sonnet 5 about $2/$10, and keep Opus 5 or Sol for the reasoning-heavy tail. The most expensive mistake in production LLM routing is running every request through a flagship because one hard case needed it. Route by difficulty, not by default. If you want a structured way to decide, Best AI Model for Coding 2026 and Best AI Model for Agents 2026 lay out the full tiering.
Here is the same decision as a routing table:
| If the request is… | Route to | Because |
|---|---|---|
| Bounded, short, high-volume (classification, routing) | Luna or Sonnet 5 | Flagship rates are wasted on short bounded answers |
| Reasoning-heavy, and the lowest ofox bill matters now | Sol | The -20% promo makes it cheaper on every line |
| Reasoning-heavy, buying at vendor list price | Opus 5 | 17% cheaper output; leads the AA index |
| Interactive, first-token latency matters | Opus 5 | ~1.9x faster to first token |
| Long streamed output, throughput matters | Sol | ~1.4x higher tokens/sec |
| Needs Pro or Ultra heavy-compute | Sol | No Opus 5 equivalent |
Try Both via ofox: A/B in 10 Lines of Code
The only comparison that settles this is the one you run on your own tasks, and the friction of running it is usually what stops people. On ofox both models sit on the same OpenAI-compatible endpoint under one key, so an A/B is a loop over two model strings. Same SDK, same billing line, no second account. The two IDs are anthropic/claude-opus-5 and openai/gpt-5.6-sol.
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")
MODELS = ["anthropic/claude-opus-5", "openai/gpt-5.6-sol"]
task = "Refactor this module for testability and explain the two riskiest changes:\n\n<paste your real code>"
for model in MODELS:
r = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": task}],
)
usage = r.usage
print(f"\n=== {model} ===")
print(r.choices[0].message.content)
print(f"[in={usage.prompt_tokens} out={usage.completion_tokens}]")
Run it across 20-30 of your real tasks, not one toy prompt, and log completion_tokens per model. Multiply that count by each model's output rate (whichever price you actually pay, list or the current ofox promo) and you are measuring quality and cost in the same loop.
Node — same shape
import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.ofox.ai/v1", apiKey: "your-ofox-key" });
const models = ["anthropic/claude-opus-5", "openai/gpt-5.6-sol"];
const task = "Find the race condition in this service and propose a fix:\n\n<paste your real code>";
for (const model of models) {
const r = await client.chat.completions.create({
model,
messages: [{ role: "user", content: task }],
});
console.log(`\n=== ${model} ===`);
console.log(r.choices[0].message.content);
console.log(`[in=${r.usage.prompt_tokens} out=${r.usage.completion_tokens}]`);
}
To exercise Opus 5's adaptive thinking or Sol's reasoning_effort, point the Anthropic-native calls at https://api.ofox.ai/anthropic with model claude-opus-5, and the Sol max-effort calls at the OpenAI Responses API. For a plain quality-and-cost A/B, the chat/completions loop above is enough, and it is the fastest way to replace a launch-day benchmark argument with a number from your own workload.
The Honest Verdict
Strip away the launch-week noise and the picture is narrow. On the one benchmark that measures both models the same way, Opus 5 is two points ahead at max effort (61 vs 59). On price the answer depends on where you buy: at vendor list price Opus 5 is ~17% cheaper on output, but on ofox right now Sol's -20% promo makes it cheaper on every line. On latency Opus 5 starts faster (~1.9x to first token); on throughput Sol streams faster (~1.4x). Everything else, the CursorBench numbers, the Terminal-Bench 91.9%, the "beats Fable 5" claims, is vendor-reported on a private harness and should move your decision by roughly nothing until you reproduce it.
That makes this a genuinely close call, not a blowout. Opus 5 has the durable edges: the neutral-composite lead, the lower first-token latency, and the drop-in path from Claude Code and Opus 4.8. Sol counters with faster streaming, the Pro and Ultra heavy-compute modes that have no Claude equivalent, and, while the promo lasts, the cheaper ofox bill. Neither should be handling your bulk classification or routing traffic; that belongs on a cost tier.
The two-point index gap is not a reason to migrate a production route on its own, and neither is a promo that can expire next week. Both are reasons to run the A/B, because the case for either model is built from small, real edges, and the only thing that turns that into a decision is your own eval. Both models are one string apart on the same key. Spend the afternoon.
References
- Artificial Analysis: Claude Opus 5 vs GPT-5.6 Sol comparison
- Artificial Analysis: GPT-5.6 has landed
- Anthropic: Claude Opus 5 announcement
- OpenAI: GPT-5.6 Sol model documentation
- Simon Willison: GPT-5.6 (July 9, 2026 GA notes)
- ofox model page: Claude Opus 5
- ofox model page: GPT-5.6 Sol
Related: Claude Opus 5 API guide — pricing, the 4.8→5 changes, and how to call it. GPT-5.6 tiers: Sol, Terra, Luna — which tier for which job. GPT-5.6 Terra vs GPT-5.5 — the half-price mid tier. Fable 5 vs Opus 4.8 vs GPT-5.5 on SWE-Bench — the Claude flagship tier on coding.
Originally published on ofox.ai/blog.
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