Claude Opus 5.5 is the better choice for difficult coding, long-horizon agents, and first-pass reliability, while GPT-6 Sol is the sharper economic choice for high-volume automation and less demanding work. The trade-off is unusually clear: Sol costs half as much per token, but independent testing places its capability ceiling below Opus 5.5.

Key Takeaways

  • Capability: Opus 5.5 leads on the strongest available coding and agent evaluations, with a maximum Artificial Analysis Intelligence Index score of about 58 versus roughly 48 for GPT-6 Sol.
  • Price: Sol costs $2 per million input tokens and $10 per million output tokens; Opus 5.5 costs $4 and $20.
  • Economics: Sol is the better default for high-volume workloads, but Opus can be cheaper overall when avoiding retries, failed tool calls, and human intervention.
  • Best use: Choose Opus 5.5 for complex repositories, production migrations, computer-use tasks, and work where failure is expensive.
  • Practical verdict: Start with Sol for routine automation and escalate hard or failed tasks to Opus 5.5.

Who Opus 5.5 is for: Engineering teams, technical operators, and researchers who value correctness, planning depth, and successful completion more than the lowest API bill.

Who GPT-6 Sol is for: Developers and platform teams processing large volumes of coding, extraction, classification, and agent tasks where a strong mid-tier model can meet the quality bar.

What launched, and what is being compared

Anthropic released Claude Opus 5.5 on September 22, 2026. OpenAI released GPT-6 Sol on the same day, alongside GPT-6 Luna; Sol is the relevant higher-capability, lower-cost GPT-6 option in this comparison. The release timing and model identities are reported by contemporary coverage, but much of the detailed benchmark material remains vendor-reported or drawn from early third-party testing.

That qualification matters. The models are new, benchmark implementations are still settling, and there is no mature consensus across a large set of independently replicated evaluations. The most useful conclusion is therefore not that one model wins every task, but that Opus 5.5 currently has more headroom while Sol has a substantially lower cost floor.

Price: GPT-6 Sol wins the spreadsheet comparison

At standard API rates, GPT-6 Sol is listed at $2 per million input tokens and $10 per million output tokens. Opus 5.5 is listed at $4 per million input tokens and $20 per million output tokens. On raw token price, Sol is exactly half the cost of Opus 5.5.

  • GPT-6 Sol: $2 input and $10 output per million tokens.
  • Claude Opus 5.5: $4 input and $20 output per million tokens.
  • Cached input: Early comparisons report $0.20 per million cached-read tokens for both models.
  • Prompt caching: Anthropic and OpenAI apply separate cache-write and cache-duration rules, so long-running agent loops need a full cost calculation rather than a simple input-price comparison.

For a workload using 10 million input tokens and 2 million output tokens, the nominal token bill is approximately $40 with Sol and $80 with Opus 5.5, before caching, tool charges, or platform fees. That difference compounds rapidly in batch processing and high-frequency agent systems.

Sol is the cheaper model by a wide margin only if both models require roughly the same amount of work to finish the task. If Opus prevents multiple failed runs or costly human review, token price alone can be misleading.

Capability and benchmark evidence

Artificial Analysis Intelligence Index

The clearest independent comparison in the available material comes from Artificial Analysis. At maximum reasoning settings, Opus 5.5 is reported at approximately 58 on the Intelligence Index, while GPT-6 Sol is approximately 48. The same analysis reports Opus 5.5 at roughly $5.98 per weighted task and Sol at about $1.06, although those figures reflect the benchmark's token usage and effort configuration rather than a universal application cost.

The result suggests a practical frontier: Sol is highly competitive for tasks below a moderate difficulty threshold, but Opus 5.5 continues improving on harder evaluations where extra reasoning and longer trajectories pay off. One early analysis placed Sol's best score around 47.5, below Opus 5.5's default medium-effort result of approximately 51.2.

Terminal-Bench

Anthropic's launch material reports Opus 5.5 at 66.4% on Terminal-Bench 4.0. Early reporting places GPT-6 Sol at approximately 44% on the same benchmark, although the available evidence indicates that OpenAI's launch table may have emphasized GPT-6 Astra or earlier Sol variants rather than presenting a perfectly symmetric Opus-versus-Sol comparison.

Terminal-Bench is particularly relevant to repository-level coding and command-line agents. It is also especially vulnerable to differences in harnesses, tool permissions, time limits, scaffolding, and effort settings. The scores should therefore be treated as directional evidence, not a definitive universal ranking.

OSWorld and computer use

OpenAI-reported results place GPT-6 Sol at about 64.4% on the offline OSWorld 2.0 evaluation at maximum effort, with a reported cost of approximately $3.25 per task. That figure is substantially below the reported cost of stronger frontier configurations, but it is a vendor-reported result and offline OSWorld scores do not automatically predict performance on every live desktop workflow.

For computer-use tasks, Sol's value proposition is compelling when the workflow is well specified and recoverable. Opus 5.5 is the safer choice when the agent must interpret ambiguous interfaces, recover from unexpected application states, or complete a sequence where one early mistake invalidates the rest.

Automation and agent benchmarks

Early reporting on AutomationBench places GPT-6 Sol around 33.2% at very high reasoning effort. OpenAI reportedly claims that Sol at xhigh effort can exceed the best result of an older Claude Opus configuration at a fraction of the cost. That comparison is not equivalent to a direct Opus 5.5 versus GPT-6 Sol test and should not be used as proof that Sol is the stronger current model.

On agents' exam-style evaluations, early tables report GPT-6 Sol reaching about 56.4% at maximum effort. These scores reinforce Sol's ability to handle structured agent tasks, but they do not resolve the more important production question: how often each model completes an unfamiliar, multi-step task without intervention.

Real-world coding and agent performance

In a 10-test real-world comparison published by Nate Herk, Opus 5.5 reportedly produced more polished results across website design, video editing, promotional-video, and dashboard-generation tasks. The observed run cost was approximately $18.32 for Opus versus $5.89 for Sol, with Opus somewhat slower.

This is useful practical evidence, but it is not a controlled scientific benchmark. The task selection, prompts, tool environment, model settings, and evaluator judgments all affect the result. It is best interpreted as evidence that Opus 5.5 may deliver a higher quality ceiling in creative and multi-step production work, not as a universal cost-per-output measurement.

Where Opus 5.5 has the advantage

  • Repository-level changes: Better suited to tasks requiring broad codebase understanding, coordinated edits, and careful regression avoidance.
  • Long-horizon planning: More likely to maintain a coherent plan across many tool calls and intermediate results.
  • Ambiguous requirements: Stronger fit when the specification is incomplete and the model must infer constraints.
  • First-pass quality: The premium can be justified when a failed run costs engineer time, cloud resources, customer impact, or a manual review.
  • Complex computer use: Preferable when recovery, visual interpretation, and state tracking matter more than raw throughput.

Where GPT-6 Sol is sufficient or preferable

  • Routine coding: Good fit for tests, straightforward features, refactors with clear acceptance criteria, and documentation.
  • High-volume agents: Lower token rates make Sol attractive for parallel task execution and automated triage.
  • Structured transformations: Extraction, normalization, tagging, summarization, and code-format conversion generally benefit from its lower cost.
  • Iteration-heavy workflows: Sol is economical when the system can validate outputs automatically and retry safely.
  • Cost-constrained products: Sol gives teams more room to increase context, parallelism, or user volume before budgets become restrictive.

Context, speed, and deployment considerations

Early GPT-6 Sol documentation summaries report a context window of approximately 1.05 million tokens. The available material does not provide an equally well-supported current context figure for Opus 5.5, so claims that one model definitively wins on context should be avoided until Anthropic's current model documentation is checked directly.

Speed comparisons are also configuration-dependent. The real-world test above found Opus 5.5 somewhat slower, while an early model comparison reports GPT-6 Sol at roughly 104.4 output tokens per second. Token throughput varies with prompt length, reasoning effort, queueing, streaming behavior, region, and provider implementation; published speed numbers should not be treated as guaranteed end-user latency.

For production systems, measure three separate quantities: time to first token, time to final answer, and time to successful task completion. A model that emits quickly but requires several retries can have worse wall-clock performance than a slower model that completes reliably on its first trajectory.

Ecosystem and workflow fit

Anthropic-oriented workflows

Opus 5.5 is the natural fit for teams already using Anthropic's API, Claude tooling, prompt-cache patterns, and agent infrastructure. Existing Claude-oriented applications can typically preserve their orchestration approach while switching to the latest Opus identifier, subject to the release's API and capability changes.

OpenAI-oriented workflows

GPT-6 Sol is the natural fit for teams standardized on OpenAI's API, tool-calling conventions, reasoning-effort controls, and surrounding production services. It is especially attractive when a single provider strategy, shared observability, and high request throughput matter more than selecting the absolute strongest model for every task.

Use a router rather than a permanent winner

The strongest architecture is often a two-tier router. Send predictable tasks to Sol, validate them with tests or structured checks, and escalate failures, ambiguity, or high-impact changes to Opus 5.5.

  1. Classify: Estimate task difficulty from repository size, tool count, ambiguity, and risk.
  2. Start cheap: Use GPT-6 Sol for routine and highly verifiable work.
  3. Validate: Run tests, schema checks, static analysis, browser assertions, or human review gates.
  4. Escalate: Retry with Opus 5.5 when Sol fails, loops, contradicts requirements, or consumes excessive effort.
  5. Track economics: Compare successful task cost, not merely tokens consumed.

How to choose in practice

Choose Opus 5.5 when

  • Failure is expensive: Production migrations, security-sensitive changes, customer-facing automation, and irreversible actions warrant the higher capability ceiling.
  • The task is underspecified: Opus is better positioned for requirements discovery and resolving conflicting constraints.
  • The repository is unfamiliar: Broad context and multi-file reasoning are more valuable than the lowest per-token price.
  • Human review is the bottleneck: Paying more for a better first pass can reduce total operating cost.

Choose GPT-6 Sol when

  • Volume dominates: Sol's $2/$10 pricing is materially better for large-scale workloads.
  • Outputs are easy to verify: Automated tests and deterministic validators make retries inexpensive.
  • Latency and parallelism matter: Sol's lower cost supports more concurrent attempts and broader coverage.
  • The quality bar is moderate: Routine engineering and structured business workflows do not always need a frontier model.

What the benchmarks do not tell you

Benchmark numbers are not interchangeable. A result may use a different reasoning setting, context budget, tool harness, model snapshot, or evaluator. Vendor claims are useful for identifying intended strengths and likely progress, but the most credible procurement decision still comes from a private test set drawn from your own tasks.

Build that test set around successful completion, not stylistic preference. Include representative repositories, realistic tool errors, long prompts, ambiguous tickets, expected output schemas, and adversarial cases. Record cost, latency, retry rate, test pass rate, and human correction time for both models.

Verdict

Claude Opus 5.5 is the better model; GPT-6 Sol is the better default economy model. Use Opus 5.5 for complex coding, long-horizon agents, ambiguous requirements, and high-consequence computer-use work where one successful run is worth more than a lower token bill. Use GPT-6 Sol for routine coding, structured transformations, high-volume automation, and workflows with strong automated validation.

For most production teams, the best answer is not choosing one exclusively: route ordinary work to Sol, measure the result, and escalate difficult or failed tasks to Opus 5.5. That strategy captures Sol's roughly 50% token-price advantage without accepting its lower capability ceiling on the tasks that matter most.

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