Claude Mythos vs Opus 4.7 vs GPT-5.5 (2026)

Quick answer. Use Claude Opus 4.7 for production coding, agentic workflows, and long-horizon engineering work — it is the strongest frontier model you can actually buy today. Use GPT-5.5 for token-efficient agentic coding, computer-use, and tight Codex/Plus budgets. Claude Mythos sits above both on capability, but it is gated to Project Glasswing partners — you cannot route production traffic to it.

April 2026 was the most compressed three-week stretch the frontier-model market has ever shipped. Anthropic announced Claude Mythos Preview on April 7. Anthropic shipped Claude Opus 4.7 nine days later on April 16. OpenAI shipped GPT-5.5 on April 23. Three frontier-class releases inside three weeks, from the two labs that matter most for builders.

The catch: only two of the three are buyable. Mythos is gated behind Project Glasswing, a security coalition Anthropic launched alongside the model. If you are an engineering lead choosing the model your team will actually use this quarter, the real question is Opus 4.7 vs GPT-5.5 — with Mythos as the wildcard that bends future roadmaps.

This guide breaks down where each model wins, where it loses, and which one fits which engineering job. If you want the deep dive on either of the publicly available models, read the Claude Opus 4.7 complete guide and the GPT-5.5 complete guide. For everything we know about Mythos so far, see the Anthropic Mythos pillar.

How do the three models compare at a glance?

The fastest way to internalise the gap between these three is a side-by-side. The numbers below are pulled from each lab's published cards and the verified third-party leaderboards (Vellum, llm-stats, Artificial Analysis) that have re-run them.

The table tells you the headline: Mythos is the capability ceiling, but it is locked behind a partner program. Opus 4.7 is the best generally-available coding model. GPT-5.5 is the best generally-available agentic-coding model when token efficiency and tool use matter more than raw architectural reasoning.

Which model is strongest at real-world coding?

This is the question that decides what your engineering team buys, so it deserves the most space.

Claude Opus 4.7 currently leads every public coding leaderboard you can verify. It scores 87.6% on SWE-bench Verified (up from 80.8% on Opus 4.6) and 64.3% on the much harder SWE-bench Pro. On CursorBench it jumps to 70% from 58%. Anecdotally, the gain compounds on tasks that need cross-file refactors, large-codebase reasoning, and multi-step debugging — the kind of work senior engineers actually do.

GPT-5.5 trades architectural depth for agentic execution. It posts 58.6% on SWE-bench Pro — behind Opus 4.7 — but takes the crown on Terminal-Bench 2.0 (82.7%), which tests end-to-end command-line workflows with planning, iteration, and tool coordination. The other half of GPT-5.5's pitch is brutal token efficiency: it uses roughly 72% fewer output tokens than Opus 4.7 on equivalent coding tasks, which compounds at scale.

Claude Mythos sits a clean tier above both. Mythos posts SWE-bench Pro 77.8%, which is 13.5 points above Opus 4.7 and 19.2 points above GPT-5.5. On adversarial security benchmarks the gap is wider — it autonomously identified a 17-year-old RCE in FreeBSD's NFS code path with no human in the loop after the initial prompt. Mythos is not just better at coding; it is better at finding the bugs everyone else missed.

Use Opus 4.7 if your team does substantial multi-file engineering, design reviews, and you want the highest end-to-end completion rate on hard tickets. Use GPT-5.5 if your bottleneck is agent loop cost — Codex, tool-calling agents, browser automation — and per-task token spend has become a line item. Use Mythos if you have a Glasswing seat and your job description is "find critical vulnerabilities before adversaries do."

What about reasoning, math, and long-horizon tasks?

The reasoning gap is where Mythos pulls genuinely far ahead of the field. On USAMO 2026 (the US Math Olympiad), Mythos scored 97.6% against Opus 4.6's 42.3% — a 55.3-point jump in a single generation. Anthropic did not publish a USAMO 2026 score for Opus 4.7 in the launch materials, so a like-for-like Mythos-vs-4.7 gap on this benchmark isn't quotable. Researchers calling this a "capability cliff" rather than a curve are not exaggerating — a 55-point jump from one model generation to the next is not the slope we have been on. (Mythos benchmarks throughout this piece are Anthropic-reported and not yet independently verified by third parties.)

For publicly accessible reasoning, Opus 4.7 leads: 94.2% on GPQA Diamond (graduate-level physics, chemistry, biology), the highest score posted by any GA model, and top of the Humanity's Last Exam leaderboard with or without tools. Anthropic's new xhigh effort level lets the model spend more inference compute when the task warrants it, and the Adaptive Reasoning / Max Effort combination scores 57.28 on the Artificial Analysis Intelligence Index.

GPT-5.5 is no slouch — its agentic-reasoning gains are the biggest jump OpenAI has shipped in a single point release. The MRCR v2 benchmark (multi-needle retrieval at 1M tokens) jumped from 36.6% on GPT-5.4 to 74.0% on GPT-5.5. That's the kind of improvement that materially changes what you can hand a model with a giant context window.

Use Opus 4.7 for hard scientific reasoning, research synthesis, and long-form analysis. Use GPT-5.5 for long-context retrieval and agentic tasks where the model needs to plan across many tool calls. Use Mythos for olympiad-grade mathematics, proof generation, and security-research synthesis — if you have access.

How do vision and multimodal capabilities stack up?

Opus 4.7 made the biggest single-version vision jump of any frontier model this cycle. It accepts images up to 2,576 pixels on the long edge — roughly 3.75 megapixels, more than 3.3x the resolution of Opus 4.6. On the XBOW visual-acuity benchmark it scores 98.5%, against 54.5% for Opus 4.6. For diagrams, screenshots, PDFs, and UI mockups, Opus 4.7 is currently the best generally-available model.

GPT-5.5 ships native multimodality across text, image, and audio, with strong screen-understanding for computer-use tasks. OpenAI's pitch is that GPT-5.5 can act on what it sees — clicking, typing, navigating — better than any model OpenAI has previously shipped. That's a different capability from Opus 4.7's higher-resolution still-image analysis.

Mythos does not currently lead multimodal benchmarks; Anthropic's published cards put it at parity with Opus 4.7 on vision tasks, with the breakthroughs concentrated in reasoning and security.

Use Opus 4.7 for design review, diagram interpretation, OCR-heavy workflows, and any task where image fidelity matters. Use GPT-5.5 for agentic computer-use, browser automation, and tasks where the model needs to actively drive a UI.

What does each model cost, and how does that change the math?

Token cost is the most underappreciated decision driver right now.

On paper, Opus 4.7 and GPT-5.5 have nearly identical input pricing at $5 per million tokens. GPT-5.5 costs more on output ($30 vs $25 per million). That implies Opus 4.7 is cheaper.

In practice, the verdict flips when you account for token efficiency. GPT-5.5 uses ~72% fewer output tokens on equivalent agentic coding tasks compared to Opus 4.7, because OpenAI tightened its scratchpad and tool-use formatting. For a single hard ticket, Opus 4.7 might produce 30K output tokens; GPT-5.5 might do it in 8K. At that point GPT-5.5 is roughly 25–40% cheaper per completed task — even with the higher per-token sticker price.

Opus 4.7 also ships a new tokenizer that consumes up to ~35% more tokens for the same text compared to Opus 4.6 — which narrows the headline cost advantage over GPT-5.5 in practice. If you are migrating from 4.6, your bill will rise even before you change anything.

Mythos pricing is $25 / $125 per million tokens — 5x Opus 4.7. Glasswing partners receive an initial $100M credit pool. If you are not a partner, the sticker price is the least of your problems: you cannot get an API key.

Use Opus 4.7 if your per-task output is bounded and you value lower-rate predictability. Use GPT-5.5 if you run high-volume agent loops and total cost per completed task is your KPI. Use Mythos if cost is not a factor and capability is.

How does the context window play out in production?

All three models advertise around 1 million input tokens. The fine print differs.

Opus 4.7 supports a 1M token input window, but prompts above 200K tokens are charged at a premium rate on the Claude API. Most production users settle at 200K to keep the bill predictable. The model holds quality across the full window better than Opus 4.6, but tooling like Claude Code still defaults the visible context window to 200K on Max plans.

GPT-5.5 has 1.05M input tokens and 128K output. In Codex (the agentic coding product), the context window is capped at 400K. The MRCR v2 benchmark suggests recall holds up much better at 1M tokens than it did on GPT-5.4 — meaningful for long-codebase or long-document workflows.

Mythos matches Opus 4.7's 1M input / 128K output. Its reasoning depth at full context is reportedly the best available, but real-world reports are scarce — Glasswing partners are largely under NDA.

Use Opus 4.7 for ~200K-token workflows where you want predictable pricing and strong recall. Use GPT-5.5 when you need to keep an entire enterprise codebase in context and the agent needs to navigate it. Use Mythos if you happen to have access and the task can absorb $125/M output.

Which model fits which developer role?

Mapping models to engineering roles is more useful than mapping them to benchmarks. Here is how we see the split at Codersera, based on the kind of work our network of vetted engineers actually ships:

• Senior backend / platform engineer. Opus 4.7. Multi-file refactors, architectural reasoning, design reviews, code review at scale.
• SRE / DevOps with heavy CLI workflows. GPT-5.5 in Codex. Terminal-Bench 2.0 dominance translates directly to shell-driven debugging.
• Mobile / frontend engineer. Opus 4.7 for design-to-code (vision), GPT-5.5 for headless-browser testing loops.
• Data engineer / ML researcher. Opus 4.7 for analysis, GPT-5.5 for long-context retrieval over large corpora.
• Security engineer / red team. Mythos via Glasswing if you can get access; otherwise Opus 4.7 with deliberate prompt scaffolding.
• Generalist startup engineer with a tight budget. GPT-5.5 if your loops are tight; Opus 4.7 if your work is fewer-but-harder tickets.

The wider point: there is no universally best model anymore. Pricing, agent harness, tool ecosystem, and the specific shape of your engineering work decide. If you are hiring engineers, the more important question is whether your team is fluent in both ecosystems — that's increasingly the bar Codersera vets against when we place developers.

What does Mythos mean for the rest of the market?

Mythos is the first model where a frontier lab said "this is too capable to release as a general product." That is a market-shaping decision. Three things to watch:

Defensive capability is starting to gate releases. Anthropic found exploitable zero-days in every major OS and every major browser using Mythos before announcing it. The Glasswing partner list — AWS, Apple, Google, Microsoft, JPMorgan, Cisco, CrowdStrike, NVIDIA, the Linux Foundation — exists because Anthropic believes the offensive symmetry of this capability is dangerous in the wild.

The capability gap between top-1 and top-3 just widened. For roughly 18 months the frontier was clustered — Opus 4.6, GPT-5.4, and Gemini 3.1 Pro were within a few points on most benchmarks. Mythos breaks the cluster. If OpenAI and Google catch up over the next two quarters, expect the gating pattern to repeat.

For everyone outside Glasswing, the practical reality is unchanged. You will be choosing between Opus 4.7 and GPT-5.5 for the rest of 2026, possibly until a public Mythos-class model ships. That's the decision this article exists to help you make.

Frequently asked questions

Can I get access to Claude Mythos as an individual developer?

No. Mythos Preview is restricted to Project Glasswing partners — a coalition of 12 launch organizations and ~40 additional vetted partners focused on critical-infrastructure security. There is no waitlist for individual developers. Anthropic has not committed to a public release date.

Which model is best for coding agents today?

For pure benchmark performance on hard real-world tickets, Claude Opus 4.7 leads — 87.6% on SWE-bench Verified, 64.3% on SWE-bench Pro. For agentic loops where token cost and tool coordination matter, GPT-5.5 is the better economic choice — strong Terminal-Bench results and 72% fewer output tokens per task. Pick based on whether your bottleneck is capability or cost.

Is GPT-5.5 cheaper than Claude Opus 4.7?

Per token, GPT-5.5 is slightly more expensive on output ($30 vs $25 per million). Per completed agentic task, GPT-5.5 is typically 25–40% cheaper because it generates many fewer output tokens. For long-form generative work, Opus 4.7 may end up cheaper. Measure your own workload.

Do all three models support a 1M token context window?

Yes. Opus 4.7 and Mythos support 1M input / 128K output. GPT-5.5 supports 1.05M input / 128K output. Practical caveats: Opus 4.7 charges a premium above 200K tokens, GPT-5.5's Codex product caps context at 400K, and effective recall at the full window differs between models — GPT-5.5's MRCR v2 score at 1M (74.0%) is the highest publicly published number.

Will Mythos eventually replace Opus 4.7 as Anthropic's flagship?

Anthropic has not committed to a timeline. Their stated framing is that Mythos is a "preview" intended for Glasswing partners, and that the model's offensive-security capability makes a broad release dangerous without industry-wide defensive readiness. A general-availability Mythos-class model is possible but unconfirmed.

Which model has the best vision capabilities?

For static images, diagrams, and screenshots, Claude Opus 4.7 leads — 98.5% on XBOW visual-acuity benchmark, 3.3x higher input resolution than Opus 4.6. For agentic computer-use (the model actively driving a UI), GPT-5.5 leads. The two capabilities are not interchangeable.

Bottom line: which one should you actually pick?

If you are choosing for a real team this quarter, the decision tree is short:

• Default to Opus 4.7 if your engineering work is multi-file, architecturally complex, or vision-heavy. It's the highest-capability model you can actually buy.
• Switch to GPT-5.5 if you live in Codex, run high-volume agent loops, or operate a budget where token efficiency matters more than the last 5% of capability.
• Watch Mythos closely. Even if you can't use it directly, its existence tells you the curve is steeper than the 2024–2025 era trained you to expect.

And whichever model your team picks, the bigger lever is whether your engineers know how to use these tools — model selection, prompt scaffolding, agent design, when to dial up effort levels, when to push back. That's increasingly the skill we look for when placing vetted remote engineers at Codersera. The frontier moves every three weeks now. The engineers who keep up are the multiplier.