Data as of October 28, 2026 · How the score is built
Claude Opus 4.6 (Adaptive)
Decision readingClaude Opus 4.6 (Adaptive) is tracked, but not publicly ranked yet. The profile exposes 3 sourced benchmark rows and leaves unsupported fields blank until a published record exists.
Released February 2026 — see all recent releases
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
Unranked
field median 50.2Not eligible for a public rank
Price
Not listed
input median $0.75No comparable first-party hosted token rate
Speed
Not measured
field median 96 tok/sTime to first token not measured
Context
1Mtokens
field median 256,000Maximum output length is tracked separately
Strongest published evidence
Coding ranks #47. Particularly well-suited for software development and code generation tasks.
Validate before choosing
3 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 3 displayable benchmark rows
Category score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticWeight 22%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| CodingRank #47 of 155Percentile 70thWeight 20%1 benchmarkVerified | 49.6 | 1 benchmark | Verified | |||
| ReasoningRank Not rankedWeight 17%2 benchmarksVerified | 66.6 | 2 benchmarks | Verified | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured |
3 of 774 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- AgenticNot measured
- Coding1/1 verified
- Reasoning2/2 verified
- MultimodalNot measured
- KnowledgeNot measured
- MultilingualNot measured
- Inst. FollowingNot measured
- MathNot measured
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Vibe Code BenchVibe Code Bench v1.1 | Score53.50% | Versus best verified row Best verified: Gemini 4 Argon · 91.90% | Gap38.4 behind | WeightDisplay only | Benchmark exact |
Reasoning2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| ARC-AGI-2Abstraction and Reasoning Corpus for AGI v2 | Score68.8% | Versus best verified row Best verified: GPT-6 Astra · 95% | Gap26.3 behind | WeightWeighted 25% | Benchmark exact |
| ARC-AGI-1ARC-AGI-1 Semi-Private Evaluation | Score93.00% | Versus best verified row Best verified: Claude Fable 5 · 98.50% | Gap5.5 behind | WeightDisplay only | Benchmark exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 3 rowsLineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- claude-opus-4-6Anthropic model ID documentation
- Context window
- 1MAnthropic model overview
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- text, imageAnthropic model overview
- Output modalities
- textAnthropic model overview
- Parameters
- Not disclosed by the provider
- Availability
- Claude APIAnthropic model overview
- Cloud regions
- Not tracked yet
- Lifecycle
- activeAnthropic model ID documentation
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Weights are not published
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
We track Claude Opus 4.6 (Adaptive), but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.
Claude Opus 4.6 (Adaptive) is a proprietary model with a 1M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Tracked from Vals.ai Vibe Code Bench v1.1 exact leaderboard data. Kept unranked until broader benchmark coverage is available.
Claude Opus 4.6 (Adaptive) sits in the Claude Opus 4.6 family with Claude Opus 4.6. 3 of 774 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #47. particularly well-suited for software development and code generation tasks.
Last updated October 28, 2026. Runtime fields remain blank until a sourced snapshot exists.
Questions
How does Claude Opus 4.6 (Adaptive) perform overall in AI benchmarks?
Claude Opus 4.6 (Adaptive) has 3 source-displayable benchmark rows, but it does not qualify for a public overall rank. The available rows remain visible by category without being converted into a site-wide score. Missing evidence stays blank instead of being estimated from an earlier model.
Is Claude Opus 4.6 (Adaptive) good for coding and programming?
Claude Opus 4.6 (Adaptive) ranks #47 out of 155 eligible models for coding and programming, with a public category score of 49.6/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Claude Opus 4.6 (Adaptive) good for reasoning and logic?
Claude Opus 4.6 (Adaptive) has source-displayable benchmark coverage for reasoning and logic, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Which sibling models are related to Claude Opus 4.6 (Adaptive)?
Claude Opus 4.6 (Adaptive) belongs to the Claude Opus 4.6 family. Related tracked variants include Claude Opus 4.6. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.
Does Claude Opus 4.6 (Adaptive) have full benchmark coverage on BenchLM?
No. Claude Opus 4.6 (Adaptive) currently has 17 source-displayable rows. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Claude Opus 4.6 (Adaptive)?
Claude Opus 4.6 (Adaptive) has a documented context window of 1M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.