Claude Mythos is the strongest coding model Anthropic has shipped. On SWE-bench Pro, the benchmark least contaminated by training data, it scores 77.8% against GPT-5.5’s 58.6%. That 19-point gap is not noise. It reflects a genuine difference in how each model handles novel, multi-step production coding problems that weren’t in anyone’s training set.

The problem is that Mythos is not available to you yet. As of June 2026, access is restricted to roughly 150 organizations in Project Glasswing. Anthropic’s May 28 statement said public release is coming in “coming weeks.” GPT-5.5, by contrast, is live today on the standard OpenAI API at $5/M input and $30/M output. If your team needs to commit to a tooling decision this week, that asymmetry matters more than any benchmark table.

This article is for senior developers who want to understand the Mythos ceiling before it drops, so when public access opens, you’re not starting from zero on the evaluation. We’ll cover every meaningful benchmark, the exact cost math per use case, where GPT-5.5 genuinely holds its own, and a decision framework you can apply the day Mythos goes public.

Claude Mythos 5 vs GPT-5.5 – Quick Verdict

Coding Benchmarks Head-to-Head

Mythos was announced April 7, 2026, with Anthropic publishing a full benchmark suite at launch. GPT-5.5 benchmark data comes from OpenAI’s technical report and independent BenchLM evaluations. Here is the full picture.

Three takeaways from this table that are worth sitting with:

• The BenchLM Coding gap (83.8 vs 58.6) is the largest single-dimension spread in the entire comparison. A 25-point gap on a normalized coding benchmark is not a marginal improvement — it reflects a categorically different performance tier on hard code generation tasks.
• The BenchLM Agentic score (82.4 vs 81.5) is essentially a statistical tie. When the orchestration framework handles task sequencing, both models perform equivalently on agent benchmarks. The quality difference concentrates in isolated, hard reasoning tasks — not in multi-step pipelines.
• Terminal-Bench 2.0 and GPQA Diamond have no GPT-5.5 published comparisons yet, which limits direct interpretation. OpenAI has not responded to requests for these scores as of this writing.

SWE-bench Pro — The Metric That Actually Matters

SWE-bench Verified is the headline number most AI companies lead with. The problem is that it has a known contamination issue: many of the underlying GitHub issues and patches were publicly available before the training cutoffs of current models. High scores on SWE-bench Verified tell you the model is good, but they don’t reliably tell you how good on genuinely unseen problems.

SWE-bench Pro addresses this directly. It uses a curated set of newer, harder issues specifically selected to reduce training contamination. It is also scored more strictly: partial credit is reduced, and trivial fixes are filtered out. This makes it the more honest measure of a model’s actual capability on production-grade code repair tasks that a developer would encounter in a real codebase.

To put 58.6% in context: GPT-5.5 is a strong model on SWE-bench Pro. It outperforms every prior GPT generation and most frontier models released before Q1 2026. The 58.6% number is not a GPT-5.5 failure. It is a Mythos anomaly. Mythos appears to handle the combination of long context, multi-file dependency resolution, and iterative test-driven repair better than any model previously evaluated on this benchmark.

For developers evaluating models on autonomous bug-fixing, PR review, or code migration tasks, SWE-bench Pro is the number to track. If you want additional context on where Mythos sits relative to earlier Claude models in this family, this breakdown of Claude Mythos vs Claude Opus 4.6 covers the intra-family progression in detail.

Where GPT-5.5 Genuinely Wins

A benchmark comparison that only highlights the leader’s strengths is not useful for tooling decisions. GPT-5.5 has real, material advantages that matter for a large portion of developer workflows. Here is where it actually wins.

• Availability. GPT-5.5 is accessible today via the standard OpenAI API. No waitlist, no enterprise approval, no Project Glasswing membership. This is not a small point — it is the single most decisive factor for any team making a tooling decision in June 2026.
• Cost. At $5/M input and $30/M output, GPT-5.5 is 5x cheaper on input and approximately 4.2x cheaper on output than Mythos. For high-frequency production use — CI pipelines, automated code review, batch analysis — this cost difference determines whether AI assistance is economically viable at scale.
• Latency. Mythos uses explicit chain-of-thought reasoning, which improves accuracy on hard multi-step problems but adds measurable latency. GPT-5.5 is faster on a per-request basis. For interactive developer tooling where response time affects flow, this matters.
• Code generation fluency. GPT-5.5 produces clean, idiomatic code across a wider range of languages with less prompting overhead. For standard code generation, autocomplete-style tasks, test writing, and cross-language translation, GPT-5.5 output quality is competitive and often marginally better on surface-level polish.
• Token efficiency. On equivalent tasks, GPT-5.5 generates approximately 72% more output per token than Opus 4.7 — a published efficiency advantage that likely reflects a similar dynamic relative to Mythos. Fewer output tokens per task means lower actual cost per completed task, narrowing the nominal price gap in practice.
• Agentic pipeline parity. On BenchLM Agentic, GPT-5.5 scores 81.5 vs Mythos’s 82.4. For agent orchestration frameworks — LangGraph, CrewAI, AutoGen — GPT-5.5 holds essentially identical performance. If your primary use case is multi-agent pipelines rather than isolated hard reasoning, the case for Mythos weakens considerably.

For a direct side-by-side on the current practical choice, this direct coding comparison of Opus 4.8 vs GPT-5.5 covers the accessible-today trade-offs in depth.

Context Window: 1M vs 128K — What the Gap Means for Real Codebases

Mythos has a 1M token context window. GPT-5.5 has a 128K context window. That is an 8x difference, and for certain classes of coding work, it is a hard capability boundary. Not a quality difference, but a binary ability gap.

• Large monorepo analysis. A mid-size production codebase — say, 200K–400K tokens of source files, tests, and configs — cannot fit in GPT-5.5’s context at all. Mythos can hold it entirely. This matters for tasks like cross-cutting refactors, dependency graph analysis, and understanding implicit contracts between distant modules.
• Long-running agentic sessions. A 20-step autonomous coding session accumulates tool call outputs, file contents, test results, and conversation history. At high step counts, GPT-5.5 hits context limits and must truncate or summarize — introducing information loss. Mythos maintains the full session state.
• Practical threshold for GPT-5.5. For most interactive developer tasks — single-file edits, function-level code review, test generation for an isolated module — 128K tokens is more than enough. The context gap becomes meaningful at roughly 50K+ tokens of active context, which corresponds to a ~200–400 file codebase being analyzed holistically.
• Retrieval as a workaround. GPT-5.5 with a well-tuned RAG pipeline can approximate some of the large-context benefit by retrieving relevant code chunks. This works reasonably well for lookup tasks but breaks down on tasks requiring implicit understanding of how components interact across the codebase — precisely where Mythos’s full-context advantage is most pronounced.

Cost Breakdown — Is Mythos Worth 4x the Price?

Mythos is priced at $25/M input tokens and $125/M output tokens. GPT-5.5 is $5/M input and $30/M output. The output cost ratio is approximately 4.2x. Here is what that looks like across three concrete use cases.

The cost math resolves differently depending on your use case. For interactive developer tooling at individual scale, the absolute dollar difference per session ($25 vs $6) is manageable if the quality justifies it. For high-frequency batch workloads, such as automated PR review running hundreds of times per day in your CI pipeline, the 4.2x cost multiplier becomes a budget constraint that requires explicit justification. And for large-context tasks where GPT-5.5 simply cannot fit the input, cost comparison is moot: Mythos is the only option.

One additional factor worth watching: Mythos’s token efficiency profile. The chain-of-thought reasoning means Mythos may use more output tokens per task than GPT-5.5 on equivalent prompts, which would widen the actual cost gap beyond the nominal 4.2x ratio. This has not been independently benchmarked with published numbers yet, but it is worth monitoring closely when Mythos becomes publicly available.

Which Model Should You Use Right Now?

If you are committing to a tool today, GPT-5.5 is the rational choice. It is available, cost-effective, and competitive on the use cases that represent the majority of developer workflows. But if your team encounters tasks where the Mythos quality ceiling would unlock work that GPT-5.5 simply cannot do, such as large-context analysis or hard autonomous code repair, build your evaluation plan now so you can move quickly when Mythos goes public.

For the current practical decision on the Claude vs GPT-5.5 spectrum, this earlier analysis of GPT-5.5 vs Claude Opus 4.7 for coding covers the accessible-today trade-offs in detail.

Frequently Asked Questions

Is Claude Mythos publicly available?

Not yet as of June 2026. Mythos was announced April 7, 2026, and is currently restricted to approximately 150 organizations across 15+ countries under Anthropic’s Project Glasswing early access program. Anthropic’s May 28, 2026 statement indicated public release is coming in “coming weeks.” There is no confirmed date.

What is Claude Mythos’s pricing?

Mythos is priced at $25 per million input tokens and $125 per million output tokens. This makes it approximately 5x more expensive on input and 4.2x more expensive on output compared to GPT-5.5, which is priced at $5/M input and $30/M output.

How significant is the SWE-bench Pro gap between Mythos and GPT-5.5?

Mythos scores 77.8% on SWE-bench Pro vs GPT-5.5’s 58.6%, a 19.2-point gap. SWE-bench Pro is considered a more reliable coding benchmark than SWE-bench Verified because it uses newer test cases with lower training contamination probability. A 19-point gap on this benchmark represents a meaningful capability difference on hard, novel production coding tasks.

Does the 1M token context window matter for most developers?

It depends on the task. For interactive coding on single files or small modules, GPT-5.5’s 128K window is sufficient. The context gap becomes a hard constraint when analyzing entire large repositories holistically, running long autonomous coding sessions that accumulate substantial history, or performing cross-cutting refactors that require understanding how distant parts of a codebase interact. If your team does not regularly work with more than ~40K lines of active context, the gap is largely theoretical.

Should I wait for Mythos or build on GPT-5.5 now?

For the vast majority of teams, building on GPT-5.5 now is the right call. It is available today, cost-efficient, and competitive on the use cases that represent most developer workloads, particularly agentic pipelines where the BenchLM Agentic scores are essentially tied (81.5 vs 82.4). The decision to wait for Mythos is justified primarily if your specific use cases require large-context analysis beyond 128K tokens or involve the class of hard, novel reasoning problems where Mythos’s chain-of-thought advantage is most pronounced.

The Bottom Line

Claude Mythos sets a new ceiling for coding AI. The SWE-bench Pro and BenchLM Coding numbers are not incremental improvements, and the 19-point gap on contamination-adjusted benchmarks is not the kind of thing you can explain away. But a ceiling you cannot access yet does not change your tooling decision today. GPT-5.5’s combination of availability, cost, and agentic parity makes it the defensible choice for most teams right now.

When Mythos goes public, the teams who have already done their evaluation groundwork will move fastest. Here is what to do in the meantime.

• Identify the two or three specific coding tasks in your workflow where model quality is the binding constraint — not cost, not latency. These are the tasks where Mythos’s benchmark advantage will translate to real productivity gains.
• Build cost tracking into your current GPT-5.5 integration so you have real output token data before you run Mythos cost comparisons at public launch.
• Watch Anthropic’s Project Glasswing announcements — the May 28 “coming weeks” statement suggests a public release window sometime in June or July 2026, and early access often comes with introductory pricing that won’t last.