Claude Opus 5 vs 4.8: Benchmarks, Cost & Upgrade
By Emma Reed
MyClaw Editorial
Compare in MyClaw
Compare Claude Opus 5 and 4.8 on your own work
Run the same task in both models to compare long-horizon reasoning, production stability, speed, and cost before choosing your default.
AI Takeaway
- Is Claude Opus 5 better than 4.8? It is substantially stronger at difficult agentic coding, long-horizon work, computer use, and professional analysis. It still will not win every narrow production workflow.
- Does Opus 5 cost more? No. Both models cost $5 per million input tokens and $25 per million output tokens through the API. Fast mode doubles those rates to $10 and $50.
- Why might the bill still change? Opus 5 turns adaptive thinking on by default, and thinking tokens are billed as output. A harder-working model can spend more on a single call even when its unit price is unchanged.
- Should you upgrade from Opus 4.8? Test Opus 5 first on complex builds, debugging, research, and automation. Keep 4.8 until your prompts, parsers, latency, and review quality pass the same checks.
- What is the fairest test? Give both models the same real task, tools, context, and acceptance criteria. Compare cost per accepted result, not price per token.
Claude Opus 5 vs 4.8 at a Glance
Opus 5 is stronger at the same published token rate, but it is not a zero-change replacement. Thinking, output length, and effort settings can affect an existing integration.
| Factor | Claude Opus 5 | Claude Opus 4.8 |
|---|---|---|
| Best fit | Complex agentic coding, long tasks, automation | Stable existing workflows and tested review pipelines |
| Standard API price | $5 input / $25 output per MTok | $5 input / $25 output per MTok |
| Fast mode | $10 input / $50 output per MTok | $10 input / $50 output per MTok |
| Context / max output | 1M / 128K | 1M / 128K |
| Thinking default | On | Off unless enabled |
| Effort levels | Low, medium, high, xhigh, max | Low, medium, high, xhigh, max |
| Minimum cacheable prompt | 512 tokens | 1,024 tokens |
Claude Opus 4.8 remains available, so you do not need to migrate every workload on launch day. Use the Claude Opus 4.8 model guide as a baseline before testing the new model.
Where Opus 5 Pulls Ahead—and Where 4.8 Still Holds Up
Opus 5 Is Built for Work That Runs Longer
The clearest gains appear when a model must keep a plan alive while using tools, encountering errors, and revising its approach. In Anthropic's launch comparison, Opus 5 scores 43.3% on Frontier-Bench v0.1, compared with 21.1% for Opus 4.8. GDPval-AA v2 scores are 1,861 and 1,593, while OSWorld 2.0 results are 70.6% and 55.7%.
The pattern favors Opus 5 for root-cause debugging, multi-file changes, browser work, tool recovery, and jobs where a superficial fix is not enough. It also verifies its work more readily.
These are vendor-reported results. Effort, prompts, tools, permissions, and the surrounding agent can change the outcome. Use the scores to choose what to test, not to predict the winner on a specific repository.
Opus 4.8 Can Still Be the Better Production Baseline
In one independent CodeRabbit test, Opus 5 produced more precise actionable comments but caught fewer known issues and generated more nitpicks. Opus 4.8 remained the more balanced option for that review workflow.
A better builder is not automatically a better reviewer. If 4.8 already delivers reliable coverage, predictable output, and manageable latency, keep it until Opus 5 wins the same test.
Claude Opus 5 vs 4.8 Cost: Same Rate, Different Bill
The API Price Is Identical
Both models charge $5 per million input tokens and $25 per million output tokens with standard global routing. A call using 100,000 input tokens and 10,000 billed output tokens costs about $0.75. US-only inference adds 10%, while Fast mode doubles the base rates to $10 and $50 and is limited to the first-party Claude API.
API billing is separate from Claude subscriptions. Opus 5 is the default on Claude Max, but monthly plan limits do not translate directly into API token costs.
Thinking Tokens Change the Real Cost
Opus 4.8 runs without thinking unless it is enabled; Opus 5 turns it on by default. Internal reasoning is billed at the output rate even when it is hidden or summarized.
If a 100K-token prompt produces 10K billed output tokens on 4.8, it costs about $0.75. If Opus 5 uses 20K thinking-plus-answer tokens, it costs about $1.00. The rate stayed fixed; the billed work increased.
That extra quarter can be good value if Opus 5 succeeds while 4.8 needs a retry. Our recent Kimi K3 vs Claude Opus 4.8 comparison reaches the same conclusion: low token rates matter only when the output is accepted.
Compare Cost per Accepted Result
Use a more complete calculation:
Cost per accepted result = model spend + retries + tool failures + human review + cost of incorrect actions
Track thinking tokens, tool calls, time, retries, and human corrections across a small task batch. That reveals whether Opus 5 is more reliable or simply spends more.
What Changes When You Upgrade From Opus 4.8
Thinking Is On Before You Ask for It
Changing the model ID also changes behavior. On Opus 5, max_tokens must leave room for thinking and the final answer. A comfortable 4.8 limit may cut Opus 5 off early.
Disabling thinking at xhigh or max effort also returns a 400 error. Keep thinking enabled or lower effort to high or below.
Recheck Prompts, Parsers, and Agent Limits
Before moving production traffic:
- Measure cost and latency at each effort level.
- Retune
max_tokensfor thinking plus visible output. - Validate tool calls, structured output, and downstream parsers.
- Check integrations that depend on server-side web fetch or Priority Tier; Opus 5 does not support them at launch.
- Ask for concise responses when length matters.
- Remove repeated verification instructions and limit unnecessary subagents.
Opus 5 can also cache prompts from 512 tokens, compared with 1,024 on 4.8. That helps shorter repeated prompts but does not replace a workload test.
Which Model Should You Use?
Choose Opus 5 for Difficult Building and Agent Work
Use Opus 5 for large features, multi-file refactors, difficult debugging, browser automation, research, spreadsheet work, and long tool loops. These are the jobs where better recovery and sustained attention can remove entire correction cycles.
It is particularly compelling inside a coding-agent workflow, where the model can inspect a repository, edit files, run tests, review the result, and continue after an error instead of returning a code snippet and stopping.
Keep Opus 4.8 When Stability Is the Product
Keep 4.8 when a production pipeline already has dependable prompts, parsers, latency limits, or review thresholds. It is also reasonable when thinking must stay disabled at higher effort, or when your own evaluation shows better coverage for a narrow job.
Easy work does not need to move simply because Opus 5 has the same unit price. If the task is repetitive or high-volume, test a lower-cost model too. A persistent managed OpenClaw environment makes that routing easier because the files, tools, and workflow can stay fixed while the model changes.
How to Test Opus 5 and 4.8 on Work You Actually Need Done
Model comparisons become more useful when everything around the model stays the same. MyClaw provides a managed, always-on OpenClaw or Hermes Agent workspace with persistent files, browser access, terminal tools, skills, memory, and schedules. Check current model availability, then run the comparison without rebuilding the environment for each attempt. Hosting and model usage are billed separately.
Step 1: Pick a Job That Can Actually Fail
Choose one bounded task with a visible finish line: fix a failing feature, research a cited competitor brief, or complete a browser workflow. Give the agent only the files, tools, and permissions it needs, then write down the acceptance checks before the run begins.
Step 2: Race Both Models Under the Same Conditions
Start clean sessions with identical prompts, context, tools, time limits, and success criteria. The goal is not to see which model writes the most polished update. It is to see which one completes the work.
Record completion, billed output and thinking tokens, tool calls, retries, elapsed time, and human corrections. If you improve the prompt after the first run, rerun both models.
Step 3: Keep the Winner and Automate the Workflow
Make the model with the lowest cost per accepted result the default for that task type. Save the successful instructions as a reusable skill or scheduled workflow. Keep the other model available for escalation when the task becomes unusually ambiguous, risky, or expensive to redo.
The result is more useful than a universal ranking: a routing rule based on work you genuinely need completed.
Conclusion: Upgrade for Hard Work, Measure Everything Else
In Claude Opus 5 vs 4.8, Opus 5 delivers much stronger long-horizon and agentic performance at the same published token rate. The upgrade is compelling for difficult coding, research, computer use, and automation, but default thinking and changed output behavior mean the migration is not free.
Test Opus 5 first where 4.8 struggles. Preserve 4.8 where it already performs reliably, and promote each model based on cost per accepted result rather than launch-day momentum.
Compare in MyClaw
Compare Claude Opus 5 and 4.8 on your own work
Run the same task in both models to compare long-horizon reasoning, production stability, speed, and cost before choosing your default.