GPT-5.6 Sol, Terra, and Luna vs Claude Sonnet 5 and Claude Fable 5: A Practical Model Selection Guide

GPT-5.6 vs Claude Sonnet 5 is not a clean prize fight. It is a routing problem.

OpenAI now presents GPT-5.6 as a family: Sol for the hardest reasoning and coding work, Terra for a balance of capability and cost, and Luna for high-volume work where unit economics matter. Anthropic answers with Claude Sonnet 5 as the practical agentic model for coding, tools, and knowledge work, plus Claude Fable 5 as the premium Claude route for long-running agents.

The wrong question is, "Which model wins?" That question feels useful because it is simple. It is also how teams end up overpaying, under-testing, or building brittle agent workflows.

The better question is more operational: which model should handle this workflow, at what effort level, with which tools, under which review rules, and with what fallback when the first answer is weak?

This guide uses public OpenAI and Anthropic documentation, Anthropic's Sonnet 5 announcement, Anthropic's Claude model overview, BrowseComp, and OSWorld-Verified. Treat it as a practical model selection guide, not a leaderboard recap.

What changed with GPT-5.6

OpenAI's documentation makes the GPT-5.6 family easier to route than a single flagship model. Sol is the top-end OpenAI choice for difficult reasoning, complex coding, architecture review, incident analysis, and synthesis-heavy work. Terra is the production middle ground. Luna is the low-cost candidate for repetitive tasks such as extraction, classification, normalization, tagging, first-pass summaries, and internal drafts.

The family also matters because OpenAI documents long context, high max output, vision-capable input, multilingual capability, and tool support across the current API stack. For teams already using the Responses API, web search, file search, function calling, or computer-use patterns, that consistency reduces routing friction.

Reasoning effort is the other major control. Do not test one Terra run and assume you know Terra. A low-effort run and a high-effort run can differ in latency, cost, persistence, and answer quality. The same warning applies to Sol and Luna.

What changed with Claude Sonnet 5 and Claude Fable 5

Anthropic positions Claude Sonnet 5 as its most agentic Sonnet model so far. The announcement describes a model that can plan, use tools such as browsers and terminals, and run autonomously at a level that previously required larger, more expensive models. Anthropic also says Sonnet 5 improves over Sonnet 4.6 on reasoning, tool use, coding, and knowledge work.

The pricing detail is not a footnote. Anthropic published launch pricing of $2 per million input tokens and $10 per million output tokens through August 31, 2026, then standard pricing of $3 per million input tokens and $15 per million output tokens. Any long-term cost model should use the standard price, with the launch price treated as a temporary discount.

Claude Fable 5 sits above Sonnet 5 in Anthropic's model table. Anthropic describes it as the most capable widely released Claude model for long-running agents. That makes Fable 5 an escalation route, not a default for every prompt.

That matches the governance lesson from Optijara's Claude Fable 5 enterprise model risk playbook: capability only helps when versioning, evaluation, fallback, and controls are explicit.

Fast comparison table

The table is a starting hypothesis, not a ranking. Long-running Claude-native work points toward Fable 5. OpenAI tool-heavy work points toward Sol. Repetitive output-heavy work may fit Luna or Sonnet 5 better than a premium route.

Benchmark evidence, handled with care

BrowseComp is an agentic browsing benchmark introduced in an arXiv paper. It focuses on difficult information-seeking tasks that can require multi-step browsing and reasoning, which makes it relevant for research agents, due diligence, procurement analysis, technical documentation review, and evidence-gathering workflows.

OSWorld-Verified evaluates computer-use agents. Its release describes it as a cleaned and more reliable version of OSWorld, built to address verification problems in desktop automation evaluation. Agents often fail in dull ways: wrong click targets, stale UI assumptions, missed confirmation steps, or narration that sounds complete when the task is not.

Anthropic's Sonnet 5 announcement references BrowseComp and OSWorld-Verified when discussing cost-performance curves across effort levels. OpenAI's documentation gives a structural view of GPT-5.6: long context, high max output, tool support, and effort controls.

The testing rule is simple. Compare models under the same effort budget, retrieval setup, tool permissions, and pass-fail rubric. A high-effort Claude run with browser access should not be compared with a low-effort OpenAI text-only run. That tells you almost nothing.

Public leaderboards can suggest what to test first. They should not decide production routing. The Optijara Arena AI evaluations guide makes the same point: rankings are inputs, not operating evidence.

The Optijara ROUTE framework for model selection

Model selection should be a routing decision. The Optijara ROUTE framework turns that into five checks: Risk, Output cost, Use of tools, Task depth, and Evaluation evidence.

ROUTE helps avoid an expensive habit: sending every task to the strongest model because it feels safer. Safety usually comes from tighter routing, narrower tools, better logs, tests, and escalation rules.

Recommended routing patterns

Use GPT-5.6 Sol when reasoning depth and OpenAI tooling matter. Test it for complex coding, architecture review, incident synthesis, policy analysis, high-stakes copilots, and workflows that depend on OpenAI web search, file search, function calling, or computer use. Watch output volume. Premium reasoning gets costly when agents retry or loop through tools.

Use GPT-5.6 Terra as the default OpenAI production candidate. Terra is a good first route for knowledge assistants, internal automation, medium-complexity coding support, proposal drafting, structured analysis, support triage, and retrieval-augmented workflows. If Terra clears the bar, reserve Sol for escalation.

Use GPT-5.6 Luna for cost-sensitive volume. Good candidates include extraction, classification, first-pass summarization, tagging, normalization, draft generation, log review, and low-risk internal transformations. Do not judge Luna by token price alone. If it causes more retries or creates summaries that mislead later steps, it may cost more in the full workflow.

Use Claude Sonnet 5 for agentic coding and tool-use efficiency. Sonnet 5 should be tested for coding agents, browser-based research agents, terminal-assisted workflows, knowledge work, and autonomous task execution where follow-through matters. Its BrowseComp and OSWorld-Verified discussion is especially relevant when your work resembles agentic search or computer-use automation.

Use Claude Fable 5 when the workload is valuable enough to pay for the premium route. That includes long-running agents, complex research, deep code modernization, high-context synthesis, multi-step planning, and Claude-native automation where Sonnet 5 misses the acceptance threshold. Buy it only when the gain shows up in your evals.

Practical evaluation checklist

Casual prompts are not an evaluation. Build a narrow test bench with real tasks and real pass criteria. For production observability, connect model choice to logs, traces, and incident review, as described in Optijara's AI inference observability guide.

A useful evaluation includes routine tasks, edge cases, adversarial cases, and expensive cases. That mix shows throughput, judgment, safety control, and real usage cost.

Token efficiency and pricing

Token price is only one part of model economics. A cheaper model can cost more if it needs retries, writes longer answers, fails tool calls, or creates human cleanup. A pricier model can cost less when it finishes in one pass.

Track input tokens, output tokens, effort setting, tool calls, latency, retries, human edits, and final acceptance rate. The key metric is cost per accepted task.

Safeguards and operational controls

Tool-capable models expand what teams can automate. They also raise the blast radius when permissions are loose.

Minimum controls should include scoped tool permissions, read-only defaults, approval gates for external writes, isolated browser sessions, sandboxed terminals, secrets redaction, prompt-injection tests, output validation, and audit logs. Add human approval for irreversible actions.

Anthropic says its Sonnet 5 safety assessments found a lower overall rate of undesirable behaviors than Sonnet 4.6 and that Sonnet 5 is generally safer in agentic contexts. Useful context, but not a substitute for your own tests.

OpenAI's tool support across GPT-5.6 also needs careful design. Function calling and computer use should be constrained by schemas, permissions, and confirmation steps. File search and web search should return evidence, not just fluent text.

Caveats and limitations

This comparison relies on public vendor documentation and public benchmark context. It does not claim private benchmark scores, unpublished Optijara client results, or guaranteed ROI. Use the latest live docs and your own tests for production decisions.

Benchmark evidence needs context. BrowseComp evaluates difficult browsing and information-seeking tasks. OSWorld-Verified evaluates computer-use agents. They are useful proxies for agentic behavior, but they do not cover every enterprise workflow, language, compliance boundary, or domain-specific reasoning pattern.

Pricing also needs context. Public token prices do not include orchestration, retrieval, logging, review time, prompt caching, batch discounts, cloud platform differences, rate limits, or failed runs.

Measurement plan for production rollout

Run the same cases across Luna, Terra, Sol, Sonnet 5, and Fable 5 where relevant. Log model ID, effort level, tool permissions, prompt version, retrieval source set, output schema, and reviewer outcome.

Machine-readable routing summary

{
  "default_route": "gpt-5.6-terra_or_claude-sonnet-5",
  "low_cost_route": "gpt-5.6-luna",
  "openai_premium_route": "gpt-5.6-sol",
  "claude_premium_route": "claude-fable-5",
  "evaluate_on": ["quality", "latency", "cost_per_accepted_task", "tool_success_rate", "evidence_accuracy", "safety_incident_rate"],
  "escalate_when": ["low confidence", "failed tool completion", "high consequence decision", "ambiguous requirements", "failed automated test", "human reviewer flags output"],
  "block_when": ["missing source evidence", "unsafe tool request", "private data boundary violation", "irreversible action without approval"]
}

Final recommendation

Do not crown one winner. Build a routed system.

GPT-5.6 Sol is the OpenAI model to test for complex reasoning, coding, and high-stakes professional work. GPT-5.6 Terra is the practical OpenAI baseline. GPT-5.6 Luna is the cost-control candidate for high-volume tasks. Claude Sonnet 5 is a serious contender for agentic coding, tool use, and knowledge work. Claude Fable 5 is the premium Claude route for long-running agents.

The best choice is the route that passes representative evals, stays inside the cost envelope, respects safety boundaries, and fails in a way the team can detect and recover from. That is not branding. It is operations.