GPT-6.1 Sol vs Luna, Claude Opus 5.5 and Grok 4.7: Cost, Benchmarks and Workload Fit

Choosing the wrong AI model can turn a small pricing difference into a major infrastructure bill. Consider a workload using 50,000 input and 5,000 output tokens per task: assuming identical token volumes, it costs roughly $0.0075 with GPT-6 Luna, $0.15 with GPT-6 Sol, $0.13 with Grok 4.7, and $0.30 with Claude Opus 5.5 before reasoning, retries, or caching.

Now multiply that across thousands of coding, support, research, or agent workflows every day.

The real question is not which model is cheapest per token, but which delivers a production-ready result at the lowest total cost per task.

Luna leads on raw cost, while Sol, Opus 5.5, and Grok 4.7 compete differently on capability, efficiency, and workload complexity. This comparison breaks down the pricing, benchmarks, speed, reasoning efficiency, and workload fit behind that decision.

Comparing GPT-6 Sol vs Luna vs Opus 5.5 vs Grok 4.7

• OpenAI documents GPT-6 Sol at $2/M input, $0.20/M cached input, and $10/M output, with a 1.05M-token context window.
• GPT-6 Luna uses the same 1.05M-token context window but costs $0.10/M input, $0.01/M cached input, and $0.50/M output.
• Anthropic lists Claude Opus 5.5 at $4/M input and $20/M output, with $0.20/M cache reads and a 1M-token context window.
• xAI lists Grok 4.7 at $2/M input, $0.50/M cached input, and $6/M output, with a 500K-token context window.

Key takeaway: Luna has the lowest raw standard API price. That does not establish the lowest production cost because task completion, reasoning, caching, tools, and retries can change the economics.

How Much Do GPT-6 Sol, Luna, Opus 5.5, and Grok 4.7 Cost?

For standard short-context requests, Luna has the most aggressive pricing. It costs 20 times less per standard input and output token than GPT-6 Sol:

• GPT-6 Luna: $0.10/M input, $0.50/M output
• GPT-6 Sol: $2/M input, $10/M output

Consider an illustrative workload with 50,000 uncached input tokens and 5,000 output tokens.

Note: These figures simply apply published rates to identical token volumes. They do not represent cost per successful task because models may consume different token amounts, reasoning tokens, or numbers of attempts.

How does long context change the cost?

OpenAI moves GPT-6 Sol and Luna into long-context pricing when a prompt exceeds 272K input tokens. The higher rates apply to the full request.

This matters for large repositories, research sessions, document collections, persistent agent histories and tool-heavy workflows that can push prompts beyond 272K tokens.

• Grok 4.7 also has a higher-context pricing tier. Below 200K prompt tokens, xAI charges $2/M input, $0.50/M cached input, and $6/M output. Above 200K, the rates rise to $4/M, $1/M, and $12/M.
• Claude Opus 5.5 supports prompt caching. Anthropic lists 5-minute cache writes at $5/M, 1-hour cache writes at $8/M, cache reads at $0.20/M, and a 50% Batch API discount on input and output.

For RAG, coding agents, and persistent assistants, caching can materially change total spend.

Why Does Cost Per Task Matter More than Token Price?

A million-token price tells you what tokens cost. It does not tell you how much useful work the model completes. A more useful production metric is:

Cost per successful task = Total inference and tool cost ÷ Accepted completed tasks

For a broader TCO calculation, teams can also include human review and correction costs. The task might be one issue fixed, one support case resolved, one invoice processed or one multi-step workflow completed.

Zapier’s AutomationBench is useful here because it evaluates end-to-end business workflows and reports both completion score and cost per task. Its environment covers 47 tools across sales, marketing, operations, support, finance and HR. The benchmark scores final environment state against fixed success criteria rather than using an LLM judge.

*As checked on 25th September 2026

These numbers do not establish a universal winner. Claude Opus 5.5 records the highest completion score among these configurations, while Sol operates at a substantially lower attempted-task cost. Luna reduces that cost further but also records lower completion scores in this benchmark. For example, Luna Max completes 20.7% of tasks at $0.04 per attempted task. Sol XHigh reaches 33.2% at $0.27, while Opus 5.5 Max reaches 42.47% at $1.44.

That makes Luna particularly interesting for workloads where attempts are inexpensive, failures are easy to retry or escalate and raw inference cost matters heavily.

What Happens When We Estimate Cost per Successful Task?

Using the published AutomationBench figures, an illustrative cost-per-success calculation gives:

AutomationBench reinforces Luna’s low-cost positioning. Luna Max costs $0.04 per attempted task with a 20.7% completion score, while Luna XHigh costs $0.02 per attempt with a 12.6% completion score. That makes Luna worth evaluating for high-volume workflows where low attempt cost and inexpensive escalation matter more than maximizing first-pass completion.

The important distinction is that cost per attempted task and cost per successful task are not the same metric. A model can be inexpensive per attempt but become costly if repeated failures create retries, escalations or human intervention.

How Should These Models Be Compared Fairly?

A credible comparison should separate:

• Official pricing and model specifications
• Benchmark results produced under the same evaluation framework
• Performance on your own production workloads

This matters because vendors and benchmark owners may use different prompts, tools, reasoning settings, budgets, retry rules, agent harnesses, and grading methods. Two percentages from different evaluation setups are not automatically comparable.

We use benchmark-owner data where model configurations are directly comparable. Vendor-published benchmark results are kept separate when the evaluation setup differs.

AutomationBench evaluates realistic business workflows and scores the resulting environment state against fixed success criteria.

DeepSWE evaluates frontier coding agents on original, long-horizon engineering tasks. Its current v1.1 leaderboard contains 113 tasks and reports score, average cost, output tokens, and agent steps.

What Do Current Benchmarks Actually Tell Us?

There is no single benchmark that provides a controlled four-way comparison of GPT-6 Sol, GPT-6 Luna, Claude Opus 5.5 and Grok 4.7 across every relevant workload.

AutomationBench currently provides directly comparable public configurations for GPT-6 Sol, GPT-6 Luna and Claude Opus 5.5. That makes it useful for comparing business-workflow completion and attempted-task economics within the same evaluation environment.

However, the same benchmark does not currently provide an equivalent Grok 4.7 configuration in the data used here. That means Grok results from vendor evaluations or separate benchmarks should not be placed beside AutomationBench percentages as though they were generated under identical conditions.

This distinction matters even when two benchmarks appear to test similar capabilities. Different agent harnesses, tool access, reasoning budgets, retry policies, task sets and scoring methods can materially change the result.

The practical approach is to choose benchmarks that resemble your production workload, then test candidate models under the same conditions.

• For coding, that may mean repository-level completion rate.
• For support, it may mean resolution rate.
• For automation, it may mean whether the final state of business systems is correct.

Public benchmarks can help narrow the candidate set. Production testing should determine the deployment decision.

What Production Costs Are Easy to Miss Beyond Token Pricing?

Standard token pricing is only one part of production economics. Two models with similar headline pricing can produce very different total costs once processing tiers, caching, reasoning, tools and retries are included.

How Do Processing Tiers Change Cost?

OpenAI documents Batch and Flex processing at 50% of Standard rates for GPT-6 Sol, and Luna. Fast mode costs 2 times the applicable rate. Eligible regional-processing endpoints carry a 10% uplift.

For GPT-6 Sol:

For GPT-6 Luna:

A batch research or back-office workload can therefore pay half the standard token rate, while a latency-sensitive Fast workload can pay twice the standard rate.

Anthropic documents a 50% Batch API discount for Claude Opus 5.5, taking standard $4/M input and $20/M output pricing to $2/M and $10/M in Batch.

xAI documents Grok 4.7 Fast at twice the standard token rates, but currently limits that variant to Cursor and Grok Build rather than the public xAI API. Its US regional endpoint carries a 10% token-price premium.

The same underlying model can therefore produce very different economics depending on latency, processing tier, and deployment region.

How Do Caching and Reasoning Change Cost?

OpenAI prices cached input at one-tenth of standard uncached input for the three GPT-6 models covered here:

Claude Opus 5.5 cache reads cost $0.20/M compared with $4/M standard input. Anthropic also lists 5-minute cache writes at $5/M and 1-hour cache writes at $8/M.

Grok 4.7 cached input costs $0.50/M compared with $2/M standard input below its 200K long-context threshold.

For applications that repeatedly send large system prompts, tool schemas, repositories or reference documents, cache-hit rate can materially affect total inference spend. Reasoning configuration also matters.

• Grok 4.7 supports low, medium, high, and xhigh, with high as its documented default.
• Claude Opus 5.5 uses adaptive thinking and defaults to medium effort.

Higher reasoning effort can increase inference cost without necessarily improving accepted-task economics. Teams should therefore test reasoning configurations against the required quality threshold rather than automatically selecting the highest available setting.

For production planning, monitor:

• Cache-hit rate
• Reasoning effort
• Tool-call frequency
• Retry rate
• Request concurrency and rate limits
• Regional or data-residency requirements
• Background versus latency-sensitive traffic
• Model escalation rate
• Human intervention after failure

These variables can change the economically preferable model even when headline token prices stay the same.

Which Model Fits Different AI Workloads in 2026?

Each model fits a different workload profile.

This does not mean every workload in a category should automatically use the listed model. It means that model is a logical candidate to include in workload-specific testing.

When Should You Evaluate GPT-6 Luna

• OpenAI positions Luna for cost-sensitive, high-volume workloads.
• Its pricing makes it a strong candidate where volume is high and the task does not require the highest capability tier.
• It is only economically cheaper if it meets the required quality threshold without excessive retries or human correction.

When Should You Evaluate GPT-6 Sol

• OpenAI recommends Sol when teams want to balance intelligence and cost.
• Its AutomationBench performance also supports evaluating it when cost per attempt is important. Sol XHigh currently records a 33.2% completion score at $0.27 per task on Zapier AutomationBench.

When Should You Evaluate Claude Opus 5.5

• Anthropic positions Opus 5.5 for long-running agentic coding and knowledge work. It supports a 1M context window, adaptive thinking, prompt caching, and a standard API price of $4/M input and $20/M output.
• That benchmark alone does not establish which model will perform better in production, but it provides a directly comparable reason to evaluate both.

When Should You Evaluate Grok 4.7

• xAI describes Grok 4.7 as a frontier model for coding, agentic tasks, and knowledge work. It supports configurable reasoning and a 500K-token context window.
• Raw token pricing does not establish cost per successful task, so businesses should measure Grok’s completion rate, reasoning consumption, retries, tool behavior, and human correction requirements directly.

How Should Businesses Benchmark These Models Before Deployment?

Start with production tasks, not generic prompts. Build a representative evaluation set from real coding tickets, customer-support requests, research jobs, document-processing tasks, or agent workflows.

For every model and reasoning configuration, measure:

• Task acceptance or completion rate
• Cost per attempted task
• Cost per successful task
• Input, output, and reasoning usage
• Cache-hit rate
• Tool-call cost
• Retry frequency
• Human correction time
• End-to-end latency
• Escalation rate to another model
• Cost avoided through first-pass success
• Execution time for multi-step agent workflows

Keep prompts, tools, budgets, retry policies, and scoring criteria as consistent as possible.

AutomationBench provides a useful methodology example by scoring the final environment state against deterministic success criteria rather than relying on another LLM to judge whether a response appears correct.

Choose the Right Model. Build the Right AI Cost Strategy with AceCloud

The cheapest model on paper is not always the cheapest model in production. For CTOs, AI teams, platform engineers, and FinOps leaders, the real decision is which model delivers an accepted result at the lowest total cost across inference, reasoning, caching, retries, latency, and infrastructure.

GPT-6 Luna, Sol, Opus 5.5, and Grok 4.7 each fit different workload profiles. The right strategy may involve one model, multiple models, or workload-based routing.

AceCloud helps teams support that strategy with GPU-first cloud infrastructure, managed Kubernetes, scalable compute, and migration support for production AI workloads.

Bring one week of token logs and a sample of production tasks. AceCloud engineers can model your cost per accepted task on your current API stack versus a self-hosted GPU setup, then let you validate the self-hosted option using ₹20,000 in free GPU credits.

Book a free consultation with AceCloudto review your workload mix and build a cost-per-task comparison before scaling.

Author Popular Post

Jason Karlin

author

Industry veteran with over 10 years of experience architecting and managing GPU-powered cloud solutions. Specializes in enabling scalable AI/ML and HPC workloads for enterprise and research applications. Former lead solutions architect for top-tier cloud providers and startups in the AI infrastructure space.

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