Claude Opus 4.6 (Non-reasoning, High Effort)

Claude Opus 4.6 is a high-priced, high-ranked model whose measured intelligence justifies testing, but not automatic adoption.

Anthropic exposes the model through the fixed API ID claude-opus-4-6, rather than a date-based alias. Anthropic also describes support for text and image input, text output, multilingual tasks, and visual capabilities in its Models overview.

The central selection question is economic. Claude Opus 4.6 costs $10 per 1M blended tokens, with input priced at $5 per 1M tokens and output priced at $25 per 1M tokens. The Anthropic pricing documentation confirms these rates and lists the model as available rather than retired.

Its Artificial Analysis Intelligence Index score is 37.8, placing it at position 78 of 578 evaluated models. Data provided by https://artificialanalysis.ai/ shows that this is a strong result, but not a leading position in the evaluated field.

That combination creates a specific profile: Claude Opus 4.6 may fit teams that value Anthropic access, multimodal input, and strong general reasoning, while teams optimizing cost or throughput should test cheaper alternatives first. The available material does not prove that Claude Opus 4.6 delivers superior coding, factual reliability, or user-perceived quality in a particular application.

Claude Opus 4.6 belongs in a selective premium tier, because its intelligence ranking is strong while nearby models offer materially lower listed prices.

The closest models in the data brief provide a useful reference frame. Gemini 3 Flash Preview (Reasoning), Nemotron 3 Ultra 550B A55B (Reasoning), and DeepSeek V4 Flash (Reasoning, High Effort) all match Claude Opus 4.6 at 37.8 on the Artificial Analysis Intelligence Index. GPT-5.2 (medium) is listed at 38, while Grok 4.3 (high) and DeepSeek V4 Flash are listed at 37.6 and 37.5. These comparisons come from Artificial Analysis.

Decision factor	Claude Opus 4.6	Nearby alternatives
Measured intelligence	Strong, with a 37.8 index score	Several nearby models have the same or similar score
Cost posture	Premium, with $10 per 1M blended tokens	Nearby listed prices range from $0.175 to $4.8125 per 1M blended tokens
Provider fit	Direct Anthropic API access and documented multimodal support	Provider capabilities and policies vary
Evidence quality	Official capability and pricing documentation is available	Practical workload evidence remains application-dependent

The ranking does not establish a universal quality winner. It indicates that Claude Opus 4.6 is competitive on a broad intelligence measure, while its price is difficult to justify for routine high-volume generation. Its strongest case is a workload where model quality, provider fit, or multimodal handling matters more than minimum token cost.

Claude Opus 4.6 appears broadly capable, but its ranking cannot answer whether it is the best model for coding, extraction, agents, or production support.

Position 78 of 578 places Claude Opus 4.6 well inside a competitive group rather than at the measured frontier. The score of 37.8 also sits beside several models with similar intelligence scores. That pattern suggests that model choice should depend on task behavior, reliability requirements, and integration constraints, not on the index alone. The ranking and score are reported by Artificial Analysis.

The data brief reports a first-token latency of 0.3s and does not report median output tokens per second. This makes the model easier to consider for interactive starts, but it leaves streaming duration unresolved. A fast first token does not show how quickly a long answer, tool trace, or code patch will finish.

Anthropic documents text and image input, text output, multilingual capability, and visual capability in its Models overview. Those documented interfaces make Claude Opus 4.6 a reasonable candidate for multimodal applications. They do not prove accuracy on image interpretation, document understanding, or multilingual production content.

The research brief found no reliable community material with disclosed prompts, samples, parameters, or evaluation methods for Claude Opus 4.6. It also found no verified public record of distinctive coding behavior, speed perception, model quirks, or concrete failure cases. Developers should therefore run task-specific tests before assigning the model a critical workflow. The most important missing evidence concerns coding success, tool-use stability, refusal behavior, long-context quality, and output completion time.

Claude Opus 4.6 is difficult to recommend for output-heavy workloads because its output rate is $25 per 1M tokens and its blended rate is $10 per 1M tokens.

The input rate is $5 per 1M tokens, so workloads that send large prompts but request short answers may fit its economics better than workloads that generate long reports, code, or agent traces. The Anthropic pricing page also lists cache writes at $6.25 per 1M tokens for five-minute storage and $10 per 1M tokens for one-hour storage, with cache reads and refreshes at $0.50 per 1M tokens.

Caching can improve repeated-context economics, but it does not remove the output cost. A system that repeatedly supplies a stable instruction set and produces compact decisions may benefit from cache reads. A system that emits verbose responses, retries tool calls, or generates long code changes remains exposed to the output price.

The nearby model data reinforces the tradeoff. Gemini 3 Flash Preview (Reasoning) is listed at $1.125 per 1M blended tokens, GPT-5.2 (medium) at $4.8125, and DeepSeek V4 Flash (Reasoning, High Effort) at $0.175. These figures are from Artificial Analysis, and they show that similar broad intelligence scores are available at lower listed blended prices.

Claude Opus 4.6 can still be cost-effective if a better answer prevents expensive human review, repeated retries, or downstream incidents. The supplied evidence does not show that it achieves those savings. Teams should measure accepted outputs, correction time, retry rate, and completed task cost rather than comparing token prices alone.

Claude Opus 4.6 is worth piloting for difficult, quality-sensitive workflows, but it should not be the default model for undifferentiated volume.

Choose Claude Opus 4.6 when the application benefits from Anthropic’s API, needs documented text and image input, handles multilingual or visual tasks, and can tolerate premium output pricing. The fixed API ID and documented multimodal interface are described in Anthropic’s Models overview.

A strong pilot target would be a workflow where an incorrect answer creates substantial review cost. Examples include complex document analysis, high-value drafting, visual reasoning, or decisions that require careful synthesis. The model’s 37.8 intelligence score supports placing it in that evaluation pool, according to Artificial Analysis.

Do not choose it solely because it carries the Opus name or because it has a competitive general score. Nearby models have similar Artificial Analysis Intelligence Index scores, and some have lower listed blended prices. Do not assume that the reported 0.3s first-token latency means fast completion, because median output speed is not provided.

The practical recommendation is a gated deployment. Compare Claude Opus 4.6 with at least one lower-cost nearby model on the same prompts, then evaluate correctness, edit distance, escalation rate, latency to completion, and cost per accepted result. The supplied research does not establish a universal winner, a specific coding advantage, or a known failure boundary. Those gaps are decision inputs, not reasons to invent certainty.

Claude Opus 4.6 requires workload-specific verification because the available public evidence describes interfaces and prices more clearly than practical failure behavior.

The research brief found no independently disclosed test set that reliably attributes coding quality, speed perception, model quirks, or repeatable failures to Claude Opus 4.6. Official pages also do not clearly specify the context window, official benchmark results, or dedicated API parameters for the Non-reasoning, High Effort variant. Anthropic’s documented model and pricing information remains useful, but it does not replace application testing. Sources include the Models overview and Pricing.