Claude Opus 5.5 vs GPT-6 Sol and Luna: Enterprise Cost-per-Task Guide 2026
Claude Opus 5.5 vs GPT-6 Sol and Luna compared for enterprise buyers: pricing, benchmarks, cost-per-task math, and which workloads to re-route this quarter.
TLDR: Anthropic and OpenAI both cut frontier-adjacent pricing within 24 hours of each other. If you run coding agents or business-workflow agents at volume, re-route to GPT-6 Sol ($2/$10 per M tokens) this quarter - it matches Claude Fable 5.1 on merge-ready code at roughly a fifth of the cost per task. If your workloads are long agentic sessions where prompt caching dominates, Claude Opus 5.5 ($4/$20, cache reads down 60% to $0.20/M) is the stronger pick. GPT-6 Luna ($0.10/$0.50) is the cheapest serious model either vendor has shipped - use it for high-volume classification, extraction, and SQL agents. Don’t benchmark-shop. Route by cost per completed task.
Why This Comparison Matters Right Now
On September 22, 2026, Anthropic shipped Claude Opus 5.5. About an hour later, OpenAI shipped GPT-6 Sol and GPT-6 Luna. Both vendors cut prices on the tier just below their flagships - Opus 5.5 is 20% cheaper per token than Opus 5, and Sol and Luna are 50% cheaper than their GPT-5.6 predecessors. This isn’t coincidence. Ramp-tracked data puts GPT-6 Astra at roughly 13% of enterprise AI spend versus 8% for Claude Fable, and Opus 5.5 is Anthropic’s direct defense of that share.
But here’s the part that matters for your budget: per-token prices lie. What you pay is cost per completed task, and on that metric the gap between flagship and efficiency tier just collapsed. OpenAI’s own numbers put GPT-6 Sol beating Claude Opus 5 on business-workflow tasks at one-eleventh the cost per task. Anthropic’s numbers put Opus 5.5 at 40% lower total cost than Opus 5 on typical workloads. Both vendors agree on one thing: the flagship tax is now optional for most enterprise work.
And there’s an irony worth exactly one sentence: both launches landed days after Anthropic’s CEO publicly proposed that the industry “pace the frontier.” The frontier, it turns out, is being paced straight into a price war.
At a Glance: Opus 5.5 vs GPT-6 Sol vs GPT-6 Luna
| Dimension | Claude Opus 5.5 | GPT-6 Sol | GPT-6 Luna |
|---|---|---|---|
| Best for | Long agentic sessions, coding, knowledge work | Business workflows, coding agents at volume | High-volume extraction, classification, SQL agents |
| Input / output per 1M tokens | $4 / $20 | $2 / $10 | $0.10 / $0.50 |
| Cached input per 1M | $0.20 (down 60%) | $0.20 | $0.01 |
| Default context | 1M tokens | 1M+ tokens (GPT-6 family) | 1M+ tokens (GPT-6 family) |
| Max output | 128,000 tokens | Higher limits vs 5.6 family | Higher limits vs 5.6 family |
| Flagship it shadows | Claude Fable 5.1 ($10/$50) | GPT-6 Astra ($10/$50) | GPT-5.6 Sol (at ~1% of the cost) |
| Terminal-Bench 4.0 (vendor-reported) | 66.4% | Not disclosed in same harness | Not disclosed |
| DeepSWE v1.1 (vendor-reported) | Not disclosed | 68.8% (max effort) | 66.6% (max effort) |
| Deployment | API, Bedrock, Vertex, Foundry, Claude plans | API, Azure, ChatGPT Work, Codex | API, ChatGPT Work, Codex, free-tier desktop |
| Safety posture | Best Anthropic alignment scores to date | Fewer coding errors, more safeguard denials | Same GPT-6 alignment stack |
Claude Opus 5.5: Fable-Level Work at 40% Less
Anthropic’s pitch is simple: Opus 5.5 performs at the level of Claude Fable 5.1 on most work and costs 40% less to run than Opus 5. The headline rate cut is 20% ($5/$25 down to $4/$20), but the real savings come from two places most buyers miss. Cache reads fell 60% to $0.20 per million tokens, and in long agentic sessions 90% or more of input tokens are served from cache. Output generation is also 30% faster, which means fewer billed seconds on latency-sensitive agent loops.
On benchmarks, Opus 5.5 leads Anthropic’s lineup in agentic coding (66.4% on Terminal-Bench 4.0, 54.4% on FrontierCode v1.1) and posts the best scores Anthropic has ever recorded on its automated behavioral audit - its internal alignment suite run across thousands of simulated scenarios. Anthropic is unusually candid that benchmark margins at this capability level are “a less reliable guide to real-world differences,” and that the gap between Opus 5.5 and Fable 5.1 is narrower than the scores suggest. That honesty cuts both ways: don’t pay the Fable premium for a gap even the vendor says is small.
The communication upgrade is the sleeper feature. Opus 5 had a reputation for verbose, hard-to-follow output. Early testers say 5.5 writes more clearly and puts the important information first. One tester’s quote: “it writes the way I do.” Clearer output is easier to audit, and easier auditing is a safety feature in regulated shops.
Where the Effort Dial Bites You
But there’s a catch, and it comes from independent testing, not the vendor. Developer Simon Willison found that Opus 5.5 at “max” thinking effort over-thinks to the point of breaking: on a simple SVG-generation test it burned through the entire 128,000-token output limit while still reasoning, returned nothing, and did it twice. Each failure cost $2.56 and took nearly 20 minutes. Fable 5.1 on max handled the same prompt fine.
What this means in practice: cap effort below max for production agent loops until Anthropic addresses this. The default “medium” effort setting is where the 40%-cheaper-than-Opus-5 math was measured, so you’re not losing the value proposition by avoiding max. But if your eval harness defaults to max effort, your cost projections will be wrong in the worst direction - unbounded reasoning spend with zero output.
Earned insight: The cache-read cut is the whole story for agentic coding. A Claude Code session re-sends the full conversation plus file context on every turn, so a 60% cut on cache reads (from $0.50 to $0.20 per million) compounds across every step of a 50-turn migration job. Anthropic’s own example - a 680,000-line code migration completed in under a day - is exactly the workload shape where caching dominates. If you’re budgeting Opus 5.5, model cache-hit rates first and headline token prices second.
Opus 5.5 Strengths:
- Fable-class output at 40% lower run cost than Opus 5, verified across Anthropic’s own workload tests
- Cache reads down 60% - the largest single cost lever for long agentic sessions
- Best alignment-audit scores Anthropic has recorded, with stronger prompt-injection resistance
- Available on Bedrock, Vertex, and Foundry - no vendor-lock-in on deployment path
Opus 5.5 Weaknesses:
- Max effort setting can over-think into the 128K output ceiling and return nothing ($2.56 per failure in independent testing)
- Still 2x the per-token price of GPT-6 Sol - the premium needs a caching or quality justification per workload
- Biology and cybersecurity tasks route through extra safeguards, with some benchmark tasks falling back to older models
- No free-tier access; subscription floor is Pro at $17-20/month
GPT-6 Sol: The Workhorse Gets Half-Price
OpenAI’s framing is cost-per-task, and the numbers are aggressive. On AutomationBench - business workflows across 47 tools spanning sales, marketing, ops, support, finance, and HR - GPT-6 Sol at max effort scores 33.2% at $0.27 per task. Claude Opus 5 at max effort scores 26.9% at 11.1 times that cost. Sol even beats low-effort GPT-6 Astra while costing roughly a quarter as much per task. For coding, Sol hits 68.8% on DeepSWE v1.1, within 1.1 points of Claude Fable 5’s best score, at approximately 80% lower cost per task.
The factuality claim deserves attention from anyone running customer-facing agents. On OpenAI’s internal evaluation built from real conversations where users flagged model mistakes, GPT-6 Sol makes about half as many factual errors as its predecessor - approaching Astra-level reliability at a fifth of the price. Hallucination rate is the hidden TCO line item in support and sales automation: every wrong answer your team has to catch and correct is labor cost the token bill doesn’t show.
Here’s the catch: GPT-5.6 models carry a scheduled 25% price increase in November. That makes the GPT-6 family’s 50% cut look even larger against the forward price - but it also means any workload you leave on 5.6 gets more expensive in weeks, not quarters. Migration isn’t optional. It’s a deadline.
Warning: GPT-5.6 Terra is now priced identically to GPT-6 Sol ($2/$12 vs $2/$10) while being worse on every published metric. If any of your routing configs still point at Terra, they’re burning money for older intelligence. Audit your model strings before November, when 5.6 prices rise another 25% and the gap gets embarrassing.
GPT-6 Sol Strengths:
- Matches Fable 5.1 on merge-ready code (FrontierCode) and business workflows (AutomationBench) at a fraction of cost per task
- Factuality roughly doubled vs predecessor - the most valuable upgrade for customer-facing agents
- Prompt-caching improvements mean more context reuse and faster responses, compounding the 50% price cut
- Available in ChatGPT Work and Codex across Plus, Pro, Business, and Enterprise tiers
GPT-6 Sol Weaknesses:
- Every headline number is vendor-reported; no independent third-party benchmarks had landed at publication time
- Still 20x the price of Luna per token - overkill for extraction and classification workloads
- Tied to OpenAI’s ecosystem for the best experience (Codex, ChatGPT Work); API-only shops get less of the stack
- The November 5.6 price hike creates migration pressure that favors OpenAI’s timeline, not yours
GPT-6 Luna: Ten Cents That Changes the Math
Luna is the most strategically interesting launch of the three. At $0.10 input and $0.50 output per million tokens - with cached input at a penny per million - it’s one of the cheapest models OpenAI has ever shipped, beaten only by far weaker nano-tier models. And unlike those nano models, Luna is genuinely capable: at max effort it scores 66.6% on DeepSWE, comparable to Claude Opus 5 and Fable 5 at medium effort, at 93-96% lower cost per task. On computer-use tasks it exceeds GPT-5.6 Sol’s medium-effort score at one-tenth the cost.
Independent developer Simon Willison - no OpenAI partisan - has already made Luna the engine of his Datasette Agent demo for SQL queries and HTML generation, calling it “fast and competent.” That’s the tell. When practitioners reach for the cheapest model unprompted within 24 hours of launch, the capability floor has moved.
What nobody tells you: Luna’s real competition isn’t Opus 5.5 or Sol. It’s your non-AI baseline. At a penny per million cached input tokens, document classification, ticket triage, log summarization, and extraction pipelines that couldn’t clear an ROI bar last quarter now clear it by an order of magnitude. The workloads Luna unlocks are the ones you never bothered to automate.
Pricing Reality: Cost per Task, Not Cost per Token
List prices verified September 23, 2026, from Anthropic’s launch announcement, OpenAI’s launch announcement, and OpenAI’s live API pricing page.
| Model | Input / 1M | Cached input / 1M | Output / 1M | Indicative cost per task |
|---|---|---|---|---|
| GPT-6 Luna | $0.10 | $0.01 | $0.50 | DeepSWE task: ~96% below Fable 5 |
| GPT-6 Sol | $2.00 | $0.20 | $10.00 | AutomationBench task: $0.27 |
| Claude Opus 5.5 | $4.00 | $0.20 | $20.00 | ~40% below Opus 5 typical workload |
| Claude Opus 5 (prior) | $5.00 | $0.50 | $25.00 | Baseline Anthropic paid before |
| GPT-5.6 Sol (prior) | $4.00 | $0.40 | $20.00 | Rising 25% in November |
| GPT-6 Astra (flagship) | $10.00 | $1.00 | $50.00 | 3.9x Sol per AutomationBench task |
| Claude Fable 5.1 (flagship) | $10.00 | $0.25 | $50.00 | 8.9x+ Sol per AutomationBench task |
Three TCO notes your finance team will ask about. First, output tokens dominate agentic bills - they’re 5x input price on every model here, and reasoning models emit a lot of them. The Opus 5.5 max-effort failure mode above is really an output-cost story. Second, cache-hit rate is the single biggest lever: at 90%+ cached input (normal for multi-turn coding agents), Opus 5.5’s effective input price drops toward $0.60/M, nearly erasing Sol’s headline advantage for session-heavy work. Third, the November GPT-5.6 price hike means any comparison against 5.6-era spend needs a 1.25x multiplier going forward - re-baseline your budgets now.
Tip: Before renegotiating your OpenAI or Anthropic commits, run one week of shadow routing: send 10% of production traffic to Sol or Opus 5.5 and measure completed-task cost, not token cost. Both vendors’ cost-per-task claims assume favorable effort settings and high cache-hit rates. Your traffic mix determines whether you actually see 40% savings or 10%. Bring your own numbers to the renewal call.
Who Should Use What - and Who Should Sit Out
Good fit for GPT-6 Sol: You’re running coding agents (Codex, internal copilots) or business-workflow agents (sales ops, support triage, finance reconciliation) at volume and your current bill is on GPT-5.6, Opus 5, or a flagship. The migration pays for itself in weeks.
Good fit for Opus 5.5: Your agents hold long sessions with heavy context reuse - multi-file refactors, research agents, knowledge-work copilots on Claude Code. The cache economics beat Sol once sessions run long. Also the pick if your deployment standard is Bedrock or Vertex and you want to stay in one cloud bill.
Good fit for GPT-6 Luna: High-volume, well-scoped tasks - classification, extraction, SQL generation, first-pass summarization. If the task spec fits in a short prompt and success is checkable, Luna is nearly free. Also the right default for free-tier and Plus users who just got flagship-adjacent quality without the seat upgrade.
Not a good fit - yet: Regulated workloads that need completed third-party evals should wait. At publication time every benchmark cited here is vendor-reported; independent evals (Artificial Analysis, Zapier’s public leaderboard updates) were still landing. If your model-risk policy requires independent verification, hold flagship routing for six weeks and revisit. And if your workloads are genuinely frontier - novel biology, adversarial security research, the hardest reasoning tasks - neither efficiency tier replaces Astra or Fable. That’s what the flagships are still for.
Bottom Line
The efficiency tier just ate the flagship’s lunch for mainstream enterprise work. GPT-6 Sol is the default re-route target for coding and workflow agents: Fable-adjacent quality at $0.27 per business-workflow task is the kind of number that ends renewal negotiations early. Claude Opus 5.5 is the pick for long-session agentic work where its 60% cache-read cut compounds hardest - and its alignment scores are genuinely the best Anthropic has published. GPT-6 Luna redefines what’s worth automating at all.
The single biggest prerequisite: measure your own cost per completed task before you commit. Vendor cost-per-task figures are real measurements on vendor-favorable settings. Your cache-hit rates, your effort settings, and your task mix decide what you actually pay. Shadow-route for a week, then sign.
Your 30-day action: audit every model string in your routing configs for GPT-5.6 Terra (dead model walking) and Opus 5 (20% overpriced as of yesterday), shadow-route 10% of traffic to Sol or Opus 5.5, and re-baseline your Q4 AI budget against the November 5.6 price hike before it hits.
FAQ
Should we replace GPT-6 Astra with GPT-6 Sol to save money?
Not wholesale. Sol beats low-effort Astra on business workflows at roughly a quarter of the cost per task, so routine coding and workflow agents should move. But Astra remains OpenAI’s best model for the hardest work - complex multi-step computer use, novel reasoning, and tasks where failure is expensive. The right pattern is tiered routing: Sol as the default, Astra as the escalation for tasks Sol fails or flags as low-confidence. That structure captures most of the savings while keeping flagship quality where it counts.
Is Claude Opus 5.5 better than GPT-6 Sol?
They win different contests. Opus 5.5 leads on Anthropic’s reported agentic-coding benchmarks (66.4% Terminal-Bench 4.0) and alignment scores, and its cache economics favor long sessions. Sol leads on reported cost per business-workflow task ($0.27 on AutomationBench) and factuality gains. Neither vendor’s numbers have independent confirmation yet. If your workload is code-heavy with long sessions, trial Opus 5.5 first. If it’s workflow-heavy at high volume, trial Sol first. Budget one engineering week for a head-to-head on your own tasks - it pays for itself.
What does GPT-6 Luna’s $0.10 pricing mean for our AI budget?
It moves the automation threshold. Tasks that cost $2-5 per thousand executions on older models now cost cents, which means classification, extraction, triage, and summarization pipelines that failed ROI review last year pass it now. The practical move is to revisit your backlog of “too expensive to automate” use cases and re-price them at Luna rates. One caution: Luna at max effort is capable, but at low effort it’s a different model - set effort floors in your routing config or quality will vary silently.
Does Opus 5.5 work on AWS Bedrock and Google Vertex?
Yes. Opus 5.5 is available through the Anthropic API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry, plus Claude Pro, Max, Team, and Enterprise plans. That multi-cloud availability is a genuine advantage over a pure-API deployment: you keep spend inside your existing cloud commit, inherit your cloud’s access controls and audit logging, and avoid a separate vendor security review. Confirm regional availability with your cloud account team before committing timelines - new models sometimes launch in limited regions first.
What is prompt caching and why does it dominate agentic AI costs?
Prompt caching lets the model reuse previously processed input - system prompts, file context, conversation history - at a steep discount instead of reprocessing full price every turn. Opus 5.5 charges $0.20 per million cached tokens versus $4.00 full price; GPT-6 Sol charges $0.20 versus $2.00. In multi-turn agent sessions, 90% or more of input tokens typically hit the cache, so the cached rate - not the headline rate - determines your bill. This is why Opus 5.5’s 60% cache-read cut matters more than its 20% headline cut.
When will Claude Sonnet 5.5 and Haiku 5.5 arrive, and should we wait?
Anthropic says Sonnet 5.5 and Haiku 5.5 follow “in the coming weeks” with the same efficiency improvements. Today’s Haiku 4.5 at $1/$5 looks badly overpriced next to GPT-6 Luna at $0.10/$0.50 - one-tenth the price - so Haiku 5.5 has real ground to make up. Don’t wait to start efficiency-tier pilots on what’s available now; structure your routing layer so swapping in Sonnet or Haiku 5.5 later is a config change, not a migration. The architecture that lets you switch models cheaply is worth more than any single model’s price.
How do we verify vendor benchmark claims before routing production traffic?
Three steps. First, ignore single-number benchmarks and replicate 50-100 of your own recent production tasks through each candidate model - your tasks are the only eval that matters. Second, watch Artificial Analysis and Zapier’s public AutomationBench leaderboard for independent numbers, which typically land 2-6 weeks after launch. Third, measure completed-task cost including retries and fallbacks, not first-attempt token cost - OpenAI’s own footnote admits the Fable 5.1 comparison omits fallback costs on 40% of tasks. Vendor benchmarks are a starting filter, never a buying decision.
What happened to GPT-5.6 Terra - should we migrate off it?
Yes, immediately. Terra was the GPT-5.6 middle tier at $2/$12; GPT-6 Sol now delivers more intelligence at $2/$10, and Terra wasn’t included in the GPT-6 generation at all. Any routing config still pointing at Terra is paying flagship-adjacent prices for a discontinued tier. Worse, all GPT-5.6 models face a scheduled 25% price increase in November, so standing still gets actively more expensive. Migration to Sol is the lowest-risk move in this whole cycle: same vendor, same API shape, lower price, better scores.
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