Claude Opus 4.7 is Anthropic’s premium 1M-context model priced at $5 per 1M input tokens and $25 per 1M output tokens. That makes it cheaper than older Opus-tier pricing such as Claude Opus 4 at $15/$75 per 1M tokens, but still materially more expensive than mainstream frontier alternatives like GPT-5 at $1.25/$10, Gemini 3 Pro at $2/$12, and Claude Sonnet 4.6 at $3/$15.
The practical question is not “is Claude Opus 4.7 expensive?” It is. The better question is whether its premium changes the economics of work that already has high human cost: senior engineering review, complex code generation, legal or technical document analysis, research synthesis, and autonomous agent tasks where one bad answer can trigger rework. At 5x the input price of GPT-5 and 2.5x the output price, Opus 4.7 needs to save time, reduce retries, or increase correctness enough to justify the delta.
This guide breaks down Claude Opus 4.7 pricing with real token math, direct comparisons, workload estimates, and routing recommendations. You will see per-task and monthly cost estimates for coding, research, long-context review, and agent workloads, plus when to use Opus 4.7 versus Claude Sonnet 4.6, GPT-5, Gemini 3 Pro, GPT-5 mini, and lower-cost models.
💡 Key Takeaway: Claude Opus 4.7 costs $5 input / $25 output per 1M tokens with a 1,000,000-token context window. Use it for high-value reasoning and review steps, not every chat turn.
Claude Opus 4.7 pricing at a glance
Claude Opus 4.7 uses a simple token pricing structure:
| Model | Provider | Input price | Output price | Context window |
|---|---|---|---|---|
| Claude Opus 4.7 | Anthropic | $5 / 1M tokens | $25 / 1M tokens | 1,000,000 |
| Claude Opus 4.6 | Anthropic | $5 / 1M tokens | $25 / 1M tokens | 1,000,000 |
| Claude Sonnet 4.6 | Anthropic | $3 / 1M tokens | $15 / 1M tokens | 1,000,000 |
| GPT-5 | OpenAI | $1.25 / 1M tokens | $10 / 1M tokens | 1,000,000 |
| Gemini 3 Pro | $2 / 1M tokens | $12 / 1M tokens | 2,000,000 |
The formula is:
Cost = input tokens × input rate + output tokens × output rate
Because rates are quoted per 1M tokens, a single request with 100,000 input tokens and 5,000 output tokens on Claude Opus 4.7 costs:
- Input: 100,000 / 1,000,000 × $5 = $0.50
- Output: 5,000 / 1,000,000 × $25 = $0.125
- Total: $0.625
For most production workloads, output tokens are the hidden cost driver. Claude Opus 4.7 output is priced at 5x its input rate. A verbose agent that writes long plans, explanations, and code patches can become expensive even if input context is controlled.
📊 Quick Math: A Claude Opus 4.7 request with 20,000 input tokens and 4,000 output tokens costs $0.20: $0.10 for input plus $0.10 for output.
How expensive is Claude Opus 4.7 versus alternatives?
Claude Opus 4.7 sits in the premium tier, but the gap depends on whether your workload is input-heavy or output-heavy.
| Model | Input / 1M | Output / 1M | Cost for 50K input + 5K output | Relative to Opus 4.7 |
|---|---|---|---|---|
| Claude Opus 4.7 | $5 | $25 | $0.375 | 1.0x |
| Claude Sonnet 4.6 | $3 | $15 | $0.225 | 40% cheaper |
| GPT-5 | $1.25 | $10 | $0.1125 | 70% cheaper |
| Gemini 3 Pro | $2 | $12 | $0.16 | 57% cheaper |
| GPT-5 mini | $0.25 | $2 | $0.0225 | 94% cheaper |
| DeepSeek V3.2 | $0.28 | $0.42 | $0.0161 | 96% cheaper |
For a medium request of 50K input tokens and 5K output tokens, Opus 4.7 costs $0.375. That is not expensive for a one-off expert task, but it becomes material at scale. At 100,000 requests per month, the same workload costs $37,500/month on Opus 4.7, compared with $22,500/month on Claude Sonnet 4.6, $11,250/month on GPT-5, and $16,000/month on Gemini 3 Pro.
The recommendation is clear: use Opus 4.7 where the quality lift is tied to business value. Do not use it as the default model for every customer support response, extraction job, summarization run, or low-risk agent step. A tiered approach is almost always cheaper: route simple work to GPT-5 mini, Gemini Flash, DeepSeek, or Sonnet, then escalate only ambiguous, high-risk, or high-value tasks to Opus.
Cache economics: where Claude Opus 4.7 can get cheaper
Prompt caching changes the economics of long-context AI. Claude Opus 4.7 has a 1,000,000-token context window, so many teams will use it with large reusable context: repositories, API docs, policy manuals, design systems, contracts, evaluation rubrics, or research corpora.
Even when a model’s headline input price is $5 per 1M tokens, repeated full-context prompts are wasteful. If your application sends the same 300K-token repository map or 500K-token documentation bundle on every request, your bill grows linearly with each run unless caching is applied at the API or application layer.
A practical cache strategy has three parts:
- Stable prefix: Keep long reusable context unchanged across requests.
- Small dynamic suffix: Put the user question, task instruction, and current file diff near the end.
- Versioned invalidation: Rebuild cached context only when the underlying repo, docs, or policy corpus changes.
Consider a coding assistant that loads 250,000 tokens of repository context and asks for a 3,000-token answer. Without caching, each Claude Opus 4.7 run costs:
- Input: 250,000 × $5 / 1M = $1.25
- Output: 3,000 × $25 / 1M = $0.075
- Total: $1.325 per run
If the reusable context is cached or reduced so that only 25,000 dynamic input tokens are billed per follow-up, the run falls to:
- Input: 25,000 × $5 / 1M = $0.125
- Output: 3,000 × $25 / 1M = $0.075
- Total: $0.20 per run
That is an 85% reduction in per-run cost before changing models.
⚠️ Warning: Long-context does not mean “send everything every time.” On Opus 4.7, repeatedly sending a 500K-token context costs $2.50 in input alone per request before the model writes a single token.
Cache economics also affect model choice. Gemini 3 Pro has a 2,000,000-token context window and costs $2 input / $12 output, so it is a strong candidate for very large input-heavy analysis. Claude Opus 4.7 makes more sense when the reasoning or writing quality of the final answer matters more than raw context ingestion price.
Scenario 1: coding assistant and code review costs
Coding workloads are usually output-heavy. The model reads issue context, relevant files, tests, previous errors, and build logs, then writes explanations, patches, or test cases. A realistic coding request can range from 10K input / 2K output for a small bug to 120K input / 8K output for multi-file refactoring.
Here are three coding workload sizes on Claude Opus 4.7:
| Coding task | Input tokens | Output tokens | Cost per task |
|---|---|---|---|
| Small bug fix | 10,000 | 2,000 | $0.10 |
| Standard PR review | 40,000 | 4,000 | $0.30 |
| Large refactor planning | 120,000 | 8,000 | $0.80 |
Monthly estimate for a 20-engineer team:
| Usage pattern | Tasks per engineer/day | Workdays/month | Monthly tasks | Opus 4.7 monthly cost |
|---|---|---|---|---|
| Light review usage | 5 | 22 | 2,200 | $660 at $0.30/task |
| Heavy coding assistant | 15 | 22 | 6,600 | $1,980 at $0.30/task |
| Refactor-heavy month | 5 | 22 | 2,200 | $1,760 at $0.80/task |
For engineering teams, Opus 4.7 is easy to justify if it saves senior developer time. At $0.30 for a standard PR review, even 10 minutes of avoided human rework is worth far more than the token cost. The risk is not individual task cost; it is unbounded agent loops that repeatedly call the model with the same large context.
Recommended routing:
- Use Claude Opus 4.7 for architectural reasoning, hard bug diagnosis, security-sensitive review, and final patch review.
- Use Claude Sonnet 4.6 for everyday code explanations and normal PR feedback.
- Use GPT-5 mini or Codex Mini for lightweight code transformations, comments, tests, and formatting.
- Use retrieval instead of full-repo context for every request.
For a direct premium comparison, Claude Sonnet 4.6 costs $3/$15, exactly 40% less than Opus 4.7 at the same token volume. If Sonnet produces acceptable code review quality for routine tasks, it should be your default. Escalate to Opus when a failed answer would waste senior engineering time or create production risk.
Scenario 2: research synthesis and analyst workflows
Research tasks are often input-heavy with moderate output. A model may read articles, meeting transcripts, market reports, or customer interviews, then produce a structured memo. Opus 4.7’s 1M-token context is useful here, but cost discipline matters because analysts tend to iterate.
Assume a research memo workflow:
- 80,000 input tokens from source documents
- 6,000 output tokens for the memo
- Claude Opus 4.7 cost: 80K × $5 / 1M + 6K × $25 / 1M = $0.55 per memo
Comparison for the same memo:
| Model | Cost per research memo | Monthly cost at 1,000 memos |
|---|---|---|
| Claude Opus 4.7 | $0.55 | $550 |
| Claude Sonnet 4.6 | $0.33 | $330 |
| GPT-5 | $0.16 | $160 |
| Gemini 3 Pro | $0.232 | $232 |
| DeepSeek V3.2 | $0.0249 | $24.90 |
Opus 4.7 is not the cheapest research model. It pays off when the output must be nuanced, defensible, and ready for executive review. For internal first-pass summaries, use a cheaper model. For the final synthesis that drives a board memo, investment recommendation, product strategy, or legal position, Opus pricing is small compared with the cost of a bad conclusion.
[stat] $550/month Estimated Claude Opus 4.7 cost for 1,000 research memos at 80K input and 6K output each
A strong research pipeline uses multiple model tiers:
- Extraction: Use a low-cost model to convert documents into structured notes.
- Clustering: Use GPT-5, Gemini 3 Pro, or Sonnet to group themes and contradictions.
- Final synthesis: Use Claude Opus 4.7 for the memo, recommendations, and caveats.
- Verification: Use a separate pass to check citations and claims.
This approach keeps Opus focused on the highest-leverage part of the workflow: synthesis quality.
Scenario 3: long-context document review
Long-context review is where token math gets real. Claude Opus 4.7 supports 1,000,000 tokens, which enables full-document review for contracts, technical specs, compliance policies, discovery sets, books, and large internal knowledge bases.
A long-context review might use:
- 400,000 input tokens
- 10,000 output tokens
- Claude Opus 4.7 cost: 400K × $5 / 1M + 10K × $25 / 1M = $2.25 per review
For many professional workflows, $2.25 is inexpensive. A human expert reviewing 400K tokens of material is expensive and slow. The larger risk is running dozens of near-identical reviews with slightly different prompts. Ten review passes cost $22.50. One thousand review passes cost $2,250.
Comparison for one 400K-input, 10K-output review:
| Model | Context window | Cost per review |
|---|---|---|
| Claude Opus 4.7 | 1,000,000 | $2.25 |
| Claude Sonnet 4.6 | 1,000,000 | $1.35 |
| GPT-5 | 1,000,000 | $0.60 |
| Gemini 3 Pro | 2,000,000 | $0.92 |
| Gemini 2.5 Pro | 2,000,000 | $0.60 |
Recommended use:
- Choose Claude Opus 4.7 for high-stakes reviews requiring deep reasoning, red-team critique, or careful writing.
- Choose Gemini 3 Pro when input size is the main constraint and you need up to 2M context.
- Choose GPT-5 when you need a lower-cost frontier default at $1.25/$10.
- Choose Claude Sonnet 4.6 when you want Anthropic-style responses at 40% lower cost.
For long-context work, the best savings tactic is hierarchical review. First split the corpus into sections and use a cheaper model to generate summaries, issue lists, and citations. Then send the condensed evidence set to Opus 4.7 for final analysis. This reduces a 400K-token prompt to perhaps 60K-100K tokens, cutting Opus input cost by 75% or more.
✅ TL;DR: Claude Opus 4.7 is economically reasonable for high-stakes long-context review at a few dollars per run, but it becomes expensive when teams repeat full-document passes instead of caching, summarizing, or routing.
Scenario 4: autonomous agent workloads
Agent workloads are the easiest way to overspend on premium models. A single “task” can involve planning, tool calls, web searches, code execution, memory retrieval, reflection, retries, and final formatting. That means the token count is not one prompt and one answer; it is a chain of calls.
A moderate agent task might consume:
- 150,000 total input tokens across all steps
- 25,000 total output tokens
- Claude Opus 4.7 cost: 150K × $5 / 1M + 25K × $25 / 1M = $1.375 per completed task
Monthly agent cost on Opus 4.7:
| Agent volume | Tasks/day | Monthly tasks | Monthly cost |
|---|---|---|---|
| Internal pilot | 50 | 1,500 | $2,062.50 |
| Department rollout | 500 | 15,000 | $20,625 |
| Production automation | 5,000 | 150,000 | $206,250 |
For comparison, the same 150K input / 25K output agent task costs:
| Model | Cost per agent task | Monthly cost at 15,000 tasks |
|---|---|---|
| Claude Opus 4.7 | $1.375 | $20,625 |
| Claude Sonnet 4.6 | $0.825 | $12,375 |
| GPT-5 | $0.4375 | $6,562.50 |
| Gemini 3 Pro | $0.60 | $9,000 |
| GPT-5 mini | $0.0875 | $1,312.50 |
| DeepSeek V3.2 | $0.0525 | $787.50 |
This is where model routing becomes mandatory. Using Opus 4.7 for every agent step can produce a six-figure monthly bill at scale. Use cheaper models for planning drafts, extraction, tool selection, classification, and simple transformations. Reserve Opus 4.7 for final decision points, complex reasoning checkpoints, and tasks that affect revenue, security, legal exposure, or customer trust.
⚠️ Warning: At 5,000 agent tasks/day, a moderate Opus 4.7 agent workload reaches about $206,250/month. Premium models must be routed, capped, and monitored at task level.
Break-even analysis: when Opus 4.7 pays for itself
Claude Opus 4.7 pays off when the extra cost is smaller than the value of better output. The delta versus alternatives is measurable.
For a 50K input / 5K output request:
- Opus 4.7: $0.375
- Claude Sonnet 4.6: $0.225
- GPT-5: $0.1125
- Gemini 3 Pro: $0.16
The incremental cost of Opus 4.7 is:
| Alternative | Extra cost per task for Opus 4.7 | Extra cost at 100K tasks/month |
|---|---|---|
| Claude Sonnet 4.6 | $0.15 | $15,000 |
| GPT-5 | $0.2625 | $26,250 |
| Gemini 3 Pro | $0.215 | $21,500 |
Use Opus 4.7 when at least one of these is true:
- The task is high-stakes. Incorrect output can cause production incidents, compliance risk, financial loss, or reputational damage.
- The task is reasoning-heavy. The model must reconcile ambiguous requirements, inspect tradeoffs, or produce a defensible recommendation.
- The output replaces expert labor. If the model saves 15 minutes of senior engineering, legal, or analyst time, the token cost is negligible.
- Retries are expensive. A cheaper model that takes three attempts may cost more in latency, orchestration, and review time.
- Final-answer quality matters. Use cheaper models for intermediate steps, then Opus for the final answer.
Do not use Opus 4.7 when the task is:
- Simple classification
- Basic summarization
- FAQ answering
- JSON extraction
- Low-risk customer support
- Bulk enrichment
- Short content rewrites
- Embedding or retrieval preprocessing
For those workloads, models like GPT-5 mini, Gemini Flash, DeepSeek V3.2, Mistral Small, or Command R deliver much lower token cost.
Recommended model selection by workload
The cheapest model is rarely the best model, and the best model is rarely the cheapest. Use this routing table as a starting point.
| Workload | Recommended default | Escalate to Opus 4.7 when |
|---|---|---|
| Routine chat support | GPT-5 mini, Gemini Flash, Command R | Customer is high-value or issue requires policy reasoning |
| Code explanation | Claude Sonnet 4.6, GPT-5 mini | Multi-file bug, architecture decision, security-sensitive code |
| PR review | Claude Sonnet 4.6 | Final review before merge to critical services |
| Research extraction | Gemini 3 Pro, GPT-5, DeepSeek V3.2 | Final synthesis needs executive-ready judgment |
| Long document review | Gemini 3 Pro, GPT-5 | Review is legal, compliance, financial, or adversarial |
| Agent planning | GPT-5 mini, DeepSeek V3.2 | Plan affects money movement, infrastructure, or external actions |
| Final agent decision | GPT-5, Claude Sonnet 4.6 | High-risk decision or ambiguous reasoning chain |
| Strategic writing | Claude Sonnet 4.6 | Board memo, investor narrative, sensitive public communication |
A practical production stack is:
- Low-cost tier: GPT-5 mini, Gemini Flash, DeepSeek V3.2 for volume.
- Standard tier: GPT-5, Gemini 3 Pro, Claude Sonnet 4.6 for most reasoning.
- Premium tier: Claude Opus 4.7 for the highest-value 5-20% of tasks.
That last number matters. If only 10% of a 100,000-task monthly workload uses Opus 4.7 and the rest uses GPT-5, your blended cost for the 50K input / 5K output task becomes:
- 10,000 Opus tasks × $0.375 = $3,750
- 90,000 GPT-5 tasks × $0.1125 = $10,125
- Blended total = $13,875
Running all 100,000 tasks on Opus would cost $37,500. Routing saves $23,625/month, or 63%, while preserving premium reasoning for the cases that need it.
How to estimate your Claude Opus 4.7 bill
Use a task-level estimate instead of a monthly guess. Start with four numbers:
- Average input tokens per task
- Average output tokens per task
- Tasks per day
- Days per month
Then apply:
Monthly cost = tasks/day × days/month × ((input tokens × $5 / 1M) + (output tokens × $25 / 1M))
Example: a product team runs 300 long analysis tasks/day, each with 60K input and 8K output, for 30 days.
- Cost per task: 60K × $5 / 1M + 8K × $25 / 1M = $0.50
- Monthly tasks: 300 × 30 = 9,000
- Monthly cost: 9,000 × $0.50 = $4,500/month
Run this estimate at three volumes: current usage, expected launch usage, and worst-case runaway usage. Add caps at the task, user, and workspace level. For agents, also cap loop count and maximum cumulative tokens per task.
You can compare live model pricing and run your own scenarios in AI Cost Check. For direct alternatives, review GPT-5 vs Claude Opus 4.6, Claude Opus 4.6 vs Gemini 3 Pro, and GPT-5 vs DeepSeek V3.2 to understand how premium, frontier, and budget models compare.
Frequently asked questions
How much does Claude Opus 4.7 cost per million tokens?
Claude Opus 4.7 costs $5 per 1M input tokens and $25 per 1M output tokens. A request with 100K input tokens and 5K output tokens costs $0.625.
Is Claude Opus 4.7 more expensive than GPT-5?
Yes. Claude Opus 4.7 costs $5/$25 per 1M tokens, while GPT-5 costs $1.25/$10 per 1M tokens. For a 50K input / 5K output request, Opus 4.7 costs $0.375 versus $0.1125 for GPT-5.
When should I use Claude Opus 4.7 instead of Claude Sonnet 4.6?
Use Claude Opus 4.7 for high-stakes reasoning, final code review, complex research synthesis, legal or compliance review, and agent decision points. Use Claude Sonnet 4.6 for everyday Anthropic workloads because it costs 40% less at $3 input / $15 output per 1M tokens.
How much does Claude Opus 4.7 cost for AI agents?
A moderate agent task using 150K input tokens and 25K output tokens costs about $1.375 on Claude Opus 4.7. At 500 tasks/day for 30 days, that is $20,625/month, so production agents should route simple steps to cheaper models.
Does Claude Opus 4.7 support long-context workloads?
Yes. Claude Opus 4.7 supports a 1,000,000-token context window. A long review with 400K input tokens and 10K output tokens costs about $2.25, making it practical for high-value document review when prompts are cached, summarized, or routed carefully.
Estimate your Claude Opus 4.7 costs
Claude Opus 4.7 is best treated as a premium reasoning model: powerful, long-context, and worth using when the output changes an expensive decision. It is not the right default for every prompt in a high-volume application.
Use AI Cost Check to calculate Claude Opus 4.7 costs for your own token counts, compare it with GPT-5, Claude Sonnet 4.6, and Gemini 3 Pro, and model monthly spend before shipping. For side-by-side pricing, start with GPT-5 vs Claude Opus 4.6 or Claude Opus 4.6 vs Gemini 3 Pro, then plug your exact workload into the calculator.