Anthropic's frontier Opus model for long-horizon agentic coding, abstract reasoning, and 1M-token enterprise workflows.
Modality:
Text
Image
PDF
model ID
anthropic/claude-opus-4.6
Output Speed
37.6
tok/s
Intelligence Index
38.8
/ 100
Context Window
1000000
tokens
Input price
30
Anytoken
Output price
150
Anytoken
Claude Opus 4.6: Frontier Reasoning and Long-Horizon Agentic Coding
Claude Opus 4.6 is Anthropic's flagship Opus-class model, released February 5, 2026, and positioned as the company's smartest model at launch for coding, enterprise agents, and knowledge work. It sits above Sonnet and Haiku in the Claude lineup and introduced a 1M-token context window and adaptive-thinking effort levels to the Opus tier. Its defining upgrade is abstract reasoning: ARC-AGI-2 nearly doubled over Opus 4.5. The model is built for agentic workflows that plan, run tools and subagents in parallel, and sustain multi-step tasks across large codebases over long sessions.
Integrate Claude Opus 4.6 via the AnyAPI.ai unified API
Performance
Where Opus 4.6 Actually Improves: Reasoning and Long-Horizon Agents
Claude Opus 4.6 is strongest at abstract reasoning and sustained agentic execution rather than raw single-shot coding. Its ARC-AGI-2 score of 68.8% nearly doubles Opus 4.5's 37.6% and surpasses competing frontier models, representing one of the largest single-benchmark improvements on a test that resists memorization. Because ARC-AGI-2 measures generalization rather than recall, this signals meaningfully better handling of novel, unseen problems. In production, that shows up as fewer dead-ends on ambiguous multi-step tasks, more reliable planning, and stronger self-correction. It plans more carefully, sustains agentic tasks for longer, operates reliably in larger codebases, and has better code review and debugging skills.
Benchmarks
Claude Opus 4.6 Benchmarks: Reasoning Leap, Coding Plateau
Independent analysis shows a split profile. On abstract reasoning, <cite index="19-8">Opus 4.6 scored 68.8% on ARC-AGI-2 versus GPT-5.2 Pro's 54.2% and Gemini 3 Pro's 45.1%.</cite> On agentic evaluations, <cite index="19-1">Terminal-Bench 2.0 reached 65.4%, OSWorld 72.7%, and BrowseComp 84.0%, showing significant leaps over Opus 4.5.</cite> Coding is effectively flat: <cite index="19-4">SWE-bench Verified scored 80.8%, slightly down from Opus 4.5's 80.9%.</cite> The practical takeaway is that Opus 4.6's value lies in reasoning, computer use, and long-context reliability rather than incremental SWE-bench gains.
Output Speed
37.6
tok/s
Intelligence Index
38.8
/ 100
MMLU
Broad world knowledge and problem-solving
0
%
GPQA
PhD-level scientific reasoning across physics, biology, chemistry.
84
%
HLE
Adherence to multi-step structured instructions.
19
%
LiveCodeBench
Tool-calling reliability in long agentic loops.
0
%
Technical Specifications
What the model supports
Claude Opus 4.6 accepts text, images, and documents such as PDFs and returns text only. <cite index="26-3,26-12">It provides a 1,000,000-token context window and supports up to 128,000 completion tokens.</cite> The two specs with the greatest production impact are the 1M context and adaptive thinking. The large window enables whole-codebase and long-document reasoning in a single call, while <cite index="22-6">four effort levels (low, medium, high, max) let Claude dynamically decide when deeper reasoning helps, with high as the default.</cite> Note the context window is distinct from the 128K per-response output cap.
Verified Specifications —
Claude Opus 4.6 (Non-reasoning, High Effort)
Input modalities
Text
Image
PDF
output modalities
Text
Context window
1000000
tokens
Maximum output tokens
128000
Reasoning
Yes
Knowledge cutoff
February 2026
Pricing (standard)
30
AnyTokens in
/
150
AnyTokens out
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"model": "anthropic/claude-opus-4.6",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
headers = {
"Authorization": "Bearer AnyAPI_API_KEY",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)import requests url = "https://api.anyapi.ai/v1/chat/completions" payload = { "model": "anthropic/claude-opus-4.6", "messages": [ { "role": "user", "content": "Hello" } ]
}
headers = { "Authorization": "Bearer AnyAPI_API_KEY", "Content-Type": "application/json"
} response = requests.post(url, json=payload, headers=headers) print(response.text)
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Code is copied
const options = {
method: 'POST',
headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'},
body: JSON.stringify({
model: 'anthropic/claude-opus-4.6',
messages: [{role: 'user', content: 'Hello'}]
})
};
fetch('https://api.anyapi.ai/v1/chat/completions', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));const options = { method: 'POST', headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'}, body: JSON.stringify({ model: 'anthropic/claude-opus-4.6', messages: [{role: 'user', content: 'Hello'}] })
}; fetch('https://api.anyapi.ai/v1/chat/completions', options) .then(res => res.json()) .then(res => console.log(res)) .catch(err => console.error(err));
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Code is copied
curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer AnyAPI_API_KEY' \
--header 'Content-Type: application/json' \
--data '
{
"model": "anthropic/claude-opus-4.6",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
'curl --request POST \ --url https://api.anyapi.ai/v1/chat/completions \ --header 'Authorization: Bearer AnyAPI_API_KEY' \ --header 'Content-Type: application/json' \ --data ' { "model": "anthropic/claude-opus-4.6", "messages": [ { "role": "user", "content": "Hello" } ] } '
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Code is copied
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Limitations & Trade-offs
1
Coding is roughly flat versus the predecessor. On SWE-bench Verified, <cite index="22-1">Opus 4.5 slightly edges out Opus 4.6 (80.9% vs 80.8%).</cite> If your primary workload is single-shot code generation measured by SWE-bench, upgrading buys little, and the top models cluster near a soft ceiling around 80%. Opus 4.6's advantages instead concentrate in reasoning, agentic planning, and long context—so evaluate on those axes rather than SWE-bench alone.
2
Premium output cost for high-volume generation. Opus 4.6 sits in Anthropic's flagship price tier, where output tokens are significantly more expensive than input, and adaptive thinking bills thinking tokens as output. Long, verbose responses and high-effort reasoning can accumulate cost quickly. For latency-sensitive or extreme-volume workloads, a Sonnet-class model or Batch API processing is more economical; reserve Opus 4.6 for tasks where reasoning quality justifies the spend.
3
1M context is beta and tiered. The full 1M-token window was introduced in beta and, per multiple sources, was gated to higher usage tiers with elevated pricing once input exceeds the standard 200K threshold. Teams relying on ultra-long-context reasoning should confirm access eligibility and the premium rate structure before designing around it, since the standard experience centers on a 200K window.
4
Text-only output and prose regression reports. Opus 4.6 accepts text, images, and documents but returns text only—no image, audio, or video generation. Additionally, <cite index="25-10">a significant portion of users report that Opus 4.6 produces flatter, more generic prose than Opus 4.5.</cite> Teams whose primary use case is creative or stylistic writing should evaluate output quality directly before migrating.
Best-Fit Workloads
Where this model earns its place
01
Long-horizon agentic coding
Opus 4.6 is built for agents that plan and execute across many steps rather than answering single prompts. It breaks complex tasks into independent subtasks, runs tools and subagents in parallel, and identifies blockers with real precision. This suits autonomous coding agents handling large refactors, multi-step debugging, and full-lifecycle development. The main constraint is cost accumulation on long agent runs, so pair with prompt caching and effort tuning.
02
Abstract reasoning and analysis
The near-doubling of ARC-AGI-2 makes Opus 4.6 well-suited to novel problems where generalization matters more than pattern recall—research synthesis, financial analysis, and complex decision support. The ARC Prize Foundation reports Opus 4.6 achieved 94.00% on ARC-AGI-1 and 69.17% on ARC-AGI-2 with high effort, state-of-the-art for both. Use high or max effort for the hardest cases, accepting the added latency and token cost.
03
Long-context document and codebase processing
The 1M-token context enables reasoning over entire codebases or large document sets in a single call. Opus 4.6 scores 76% on MRCR v2 (8-needle, 1M context) compared to Sonnet 4.5's 18.5%, a qualitative shift in long-context reliability. This benefits migration analysis, large-scale code review, and multi-document research. Confirm tier eligibility and premium rates for the full 1M window before building around it.
04
Computer use and agentic navigation
Anthropic positions Opus 4.6 as setting a new standard for computer use, and independent testing supports this: OSWorld reached 72.7% and BrowseComp 84.0%, significant leaps over Opus 4.5 and competing models. This suits agents that navigate GUIs, browsers, and multi-application workflows. Because these tasks are long-running and tool-heavy, monitor token consumption and use caching to keep costs predictable.
Integration
01
One API integration
02
Easy model switching
03
Flexible for production
04