Reviewed 11 October 2026
GPT-5.1 and Claude 4.5 belong to an earlier comparison, not the newest October 2026 frontier lineup. The useful enterprise question remains: which exact model configuration produces an accepted result at the required latency, risk and operating cost? This refresh removes unsupported private benchmark scores and replaces them with a reviewable selection method.
Direct answer: keep validated historical models only when lifecycle and task evidence justify them. Compare current candidates on your own workflow before migrating, and preserve a rollback configuration.
Historical names and current catalogs
The OpenAI API catalog now highlights GPT-6 Astra, GPT-6.1 Sol and GPT-6 Luna. The Claude catalog includes Fable 5.1, Opus 5.5, Sonnet 5.5 and Haiku 5.5. These live catalogs establish that the older names in this URL should not be described as the latest models.
Record the exact provider, model identifier, platform and lifecycle status. A model available through one platform may have a different identifier or service configuration elsewhere. A product subscription is also different from API billing. Do not mix the cost of a chat seat with the cost of application inference.
An existing integration can remain useful after newer models appear. The decision depends on retirement status, quality, latency and migration effort. Test the current catalog as a source of candidates rather than treating every new release as an obligation to switch. Conversely, a deprecated model needs a migration plan even if the previous evaluation was strong.
The earlier article’s claimed stress tests and customer outcomes are not retained because no inspectable task corpus or logs were supplied. The evaluation below is a procedure you can run, not a report that this site secretly benchmarked your enterprise workload.
Current standard rates with a defined scope
The following USD rates are standard text API input/output rates checked 11 October 2026. They exclude caching, batch discounts, tool charges, platform premiums and tax. OpenAI’s catalog and Anthropic’s pricing provide the relevant billing surfaces. Check the specific endpoint before budgeting.
| Model | Input per 1 M tokens | Output per 1 M tokens |
|---|---|---|
| GPT-6.1 Sol | $2 | $10 |
| Claude Sonnet 5.5 | $2 | $10 |
| Claude Opus 5.5 | $4 | $20 |
Identical token rates do not imply identical cost per accepted task. Models tokenize content differently and may use different output lengths, retries or effort. Context capacity also does not guarantee accurate retrieval from a long document. A reliable comparison measures the entire task rather than selecting a winner from the price column.
Build an enterprise acceptance set
Select cases from the actual workflow and separate development examples from a held-out acceptance set. Include ordinary cases, missing data, conflicting instructions, long context and tool failures. For extraction, retain expected fields and exact source references. For support, retain valid policy and escalation outcomes. For coding, retain meaningful tests and security requirements.
Score factual correctness, required-format validity and permitted action behaviour separately. Valid JSON can contain a wrong amount or an unauthorized record identifier. A tool-call success code can still refer to the wrong customer. Ask whether the final authoritative state matches the requested and permitted action.
Run each model with documented settings. Equal temperature is not a universal fairness rule because models support different controls. Use supported parameters and match the intended operating budget. Record elapsed time, p95 latency under representative concurrency, input/output usage and human review. Repeat enough cases to observe variability rather than reporting a precise percentage from a tiny sample.
Treat critical failures as gates. A cross-tenant disclosure or unauthorized financial action cannot be averaged away by good writing scores. Keep the prompts, outputs and judgments inspectable. This turns model selection into an engineering decision rather than a claim about a model’s personality.
Illustrative cost per accepted result
Assume 10,000 monthly attempts, each with 2,000 input and 500 output tokens. At $2/$10 per million, the uncached token cost is 20 million×$2+5 million×$10=$90. At $4/$20, the same token counts cost $180. These are arithmetic under explicit assumptions, not predictions of identical token usage or quality.
Now include quality. Suppose a candidate requires 1,000 reviewer minutes at $30 per hour, adding $500. Its operating subtotal becomes $590 before platform overhead. If only 9,500 attempts are accepted, the cost is about $0.062 per accepted result. A different candidate with a higher token bill but substantially less review could have a lower accepted-unit cost.
Run sensitivity for retries, output expansion and low-volume overhead. A long-context use case may need an entirely different token distribution. Avoid claiming a universal model saving from an example with short prompts. The acceptance denominator and cost boundary should be visible beside every result.
The same worksheet should include the cost of moving prompts, tool schemas, monitoring and operational runbooks. A migration that saves $50 monthly but requires several days of work may be inappropriate unless it also resolves lifecycle or quality problems.
Roll out with a reversible boundary
Keep the last accepted model configuration available while testing a candidate. Begin with read-only or draft tasks, then add a limited cohort. Compare accepted outcomes and inspect failure clusters. Monitor the exact model version and usage so a provider alias change does not silently invalidate the comparison.
Version prompts and tool contracts. Test missing fields, unsupported parameters, timeouts and ambiguous write results. Require post-action verification and escalation for uncertain state. Establish rollback triggers tied to quality, latency and risk, rather than waiting for a large incident.
Use the current model guide for broader catalog context and action guardrails for consequential integrations. The useful next step is to retain a small inspectable acceptance set and run it against one current candidate plus your incumbent.
Frequently asked questions
Are GPT-5.1 and Claude 4.5 the latest models?
No. The current official catalogs list newer families. This URL is retained as historical context with a current selection method.
Do identical API rates mean identical operating cost?
No. Tokenization, output length, retries, review and acceptance rates affect cost per completed task.
Should every new release trigger migration?
No. Evaluate task quality, lifecycle, latency, risk and switching cost. Keep a rollback configuration.
What makes an enterprise benchmark reviewable?
Exact model settings, representative held-out cases, retained outputs, explicit scoring and authoritative action-state checks.
Sources checked 11 October 2026
Research Path