Claude Opus 4.6 benchmarks
Claude Opus 4.6 is a proprietary model from Anthropic, released 5 Feb 2026. It ranks 17th of 51 on our overall leaderboard and 22nd of 51 for long context. At A$14.04 per million tokens (blended), it costs 3.9× the median model we track. It can run with requests kept in Australia on AWS Bedrock in Sydney and Melbourne.
- CONTEXT WINDOW
- 1,000,000 tokens
- MAX OUTPUT
- 128,000 tokens
- INPUT
- text, image
- WEIGHTS
- Proprietary
- RELEASED
- 5 Feb 2026
Claude Opus 4.6 across our rankings.
Benchmark results.
Independent test scores are for the adaptive reasoning, max effort setting. “Among tracked” ranks Claude Opus 4.6 against the 51 models on this site; LMArena ranks run across its full leaderboard.
| BENCHMARK | RESULT | AMONG TRACKED | SOURCE DETAIL |
|---|---|---|---|
| INDEPENDENT TESTS · ARTIFICIAL ANALYSIS | |||
| AA Intelligence Index | 31.9 | #38 of 51 | Adaptive reasoning, max effort |
| GPQA Diamond | 89.6% | #36 of 44 | Adaptive reasoning, max effort |
| Humanity’s Last Exam | 39.9% | #34 of 51 | Adaptive reasoning, max effort |
| AA-LCR | 78.0% | #41 of 51 | Adaptive reasoning, max effort |
| BLIND HUMAN VOTES · LMARENA | |||
| LMArena Text71,993 votes · ±4 | 1505 | #2 of 42 | #2 of 402 on LMArena · 13 Sep 2026 |
| LMArena WebDev18,328 votes · ±6 | 1547 | #24 of 45 | #29 of 129 on LMArena · 22 Sep 2026 |
| LMArena Vision20,835 votes · ±7 | 1299 | #4 of 33 | #5 of 152 on LMArena · 13 Sep 2026 |
| LMArena Document27,929 votes · ±7 | 1495 | #2 of 26 | #2 of 44 on LMArena · 13 Sep 2026 |
| LMArena Search134,699 votes · ±5 | 1223 | #4 of 12 | #4 of 34 on LMArena · 24 Aug 2026 |
What Claude Opus 4.6 costs in Australian dollars.
List prices converted at the snapshot’s RBA rate, excluding GST. Reasoning models are billed for their thinking as output tokens, so heavier settings cost more than the job estimates show.
| PER MILLION TOKENS | AUD | USD LIST |
|---|---|---|
| Input tokens | A$7.02 | US$5.00 |
| Output tokens | A$35.10 | US$25.00 |
| Blended (3 in : 1 out) | A$14.04 | US$10.00 |
| JOB | COST (AUD) | MEDIAN MODEL |
|---|---|---|
| Customer support replyPER 1,000 REPLIES | A$24.57 | ≈ A$6.58 |
| Summarise a 30-page documentPER 100 DOCUMENTS | A$16.85 | ≈ A$4.74 |
| Agentic coding taskPER 10 TASKS | A$13.34 | ≈ A$3.72 |
| SETTING | INTELLIGENCE | A$ / 1M | SPEED |
|---|---|---|---|
| Adaptive reasoning, max effort | 31.9 | A$14.04 | |
| Non-reasoning, high effort | 26.4 | A$14.04 |
Running Claude Opus 4.6 in Australia.
It can run with requests kept in Australia on AWS Bedrock in Sydney and Melbourne. How the platforms compare →
| PLATFORM | AVAILABILITY | DETAIL |
|---|---|---|
| AWS Bedrock · Sydney | AU ROUTING | |
| AWS Bedrock · Melbourne | AU ROUTING | |
| Azure · Australia East | NOT IN AU | |
| Google Vertex AI · Sydney | GLOBAL |
Compare it with.
Claude Opus 4.6, answered.
How much does Claude Opus 4.6 cost in Australian dollars?
Claude Opus 4.6 lists at US$5.00 per million input tokens and US$25.00 per million output tokens — A$7.02 and A$35.10 at A$1 = US$0.7123 (RBA, 22 Sep 2026), excluding GST. A typical customer support reply works out at about A$24.57 per 1,000 replies, before any reasoning tokens.
Can I use Claude Opus 4.6 in Australia with data kept onshore?
Claude Opus 4.6 can run with requests kept in Australia on AWS Bedrock in Sydney and Melbourne. Availability comes from the cloud providers’ own documentation; check your provider’s current terms before relying on it for data-residency obligations.
What is Claude Opus 4.6’s context window?
Anthropic documents a context window of 1,000,000 tokens (1M), with up to 128,000 output tokens. On AA-LCR, which tests reasoning across ~100,000-token document sets, it scores 78.0%.
Ratings: LMArena leaderboard dataset (CC BY 4.0), rescaled for the lens scores · leaderboards to 22 Sep 2026
Evaluations, prices and speed: Artificial Analysis (artificialanalysis.ai) · fetched 23 Sep 2026
Exchange rate: Reserve Bank of Australia, table F11.1 · A$1 = US$0.7123 on 22 Sep 2026 · prices exclude GST
Snapshot 23 Sep 2026 · updated weekly · How the rankings work →