Anthropic Claude Opus 5 vs Claude Opus 4.6

Anthropic Claude Opus 5 (Amazon Bedrock, 1,000,000-token context) versus Claude Opus 4.6 (Azure AI Foundry, 1,000,000-token context). Claude Opus 4.6 is cheaper by 9% on a blended token mix. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.

Bottom line — Anthropic Claude Opus 5 vs Claude Opus 4.6

Anthropic Claude Opus 5 and Claude Opus 4.6 are priced within 9% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.

For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.

Side-by-side cost

Live workload comparison

Same workload run through both models. The cheaper one is highlighted.

3,000
01,000,000
400
0128,000
5,000
01,000,000
Amazon Bedrock
$4,185/mo
Input $5.50/M · Output $27.50/M
Azure AI Foundry
$3,805/mo
Input $5.00/M · Output $25.00/M
At this workload, Claude Opus 4.6 is 9% cheaper than Anthropic Claude Opus 5 — a savings of $380/month ($4,566/year).
Production recipe — Agent Command Center
strategy: cost-optimized
primary:
  model: claude-opus-4-6
  provider: azure-ai-foundry
fallback:
  model: au-anthropic-claude-opus-5
  provider: bedrock
shadow: { sample_rate: 0.05 }   # mirror 5% of traffic to compare quality live
Anthropic Claude Opus 5 Claude Opus 4.6
Input price $5.50/M $5.00/M
Output price $27.50/M $25.00/M
Context window 1,000,000 1,000,000
Max output 128,000 128,000
Function calling
Vision
Audio input
Reasoning
Prompt caching
Structured output
Pricing verified Aug 6, 2026 Aug 6, 2026
Cheaper option
~9% cheaper than the priciest in this pair
Larger context
1,000,000 tokens
More capabilities
5 of 6 capability flags advertised

Benchmark comparison

Side-by-side public benchmark scores. Greener bar = winner.

Chatbot Arena ELOgeneral
Anthropic Claude Opus 5
Claude Opus 4.6
1,498
τ-bench (retail)agent
Anthropic Claude Opus 5
Claude Opus 4.6
91.9%
GPQA Diamondreasoning
Anthropic Claude Opus 5
Claude Opus 4.6
91.3%
MMLUgeneral
Anthropic Claude Opus 5
Claude Opus 4.6
91.1%
SWE-bench Verifiedagent
Anthropic Claude Opus 5
Claude Opus 4.6
81.4%
SWE-benchagent
Anthropic Claude Opus 5
Claude Opus 4.6
77.8%
MMMU-Promultimodal
Anthropic Claude Opus 5
Claude Opus 4.6
73.9%
ARC-AGI-2reasoning
Anthropic Claude Opus 5
Claude Opus 4.6
68.8%
Humanity's Last Examreasoning
Anthropic Claude Opus 5
Claude Opus 4.6
53.0%

Cost at scale: monthly spend at three usage volumes

Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.

Scale Anthropic Claude Opus 5 Claude Opus 4.6 Delta
Startup
10K requests/day
$3,300 /mo $3,000 /mo $300/mo
Mid-market
100K requests/day
$33,000 /mo $30,000 /mo $3,000/mo
Enterprise
1M requests/day
$330,000 /mo $300,000 /mo $30,000/mo

At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.

Migration considerations

Concrete differences to wire through your stack before you flip traffic from one to the other.

  • Provider changes from Amazon Bedrock to Azure AI Foundry. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.

How to A/B test Anthropic Claude Opus 5 vs Claude Opus 4.6 in production

If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:

  1. 1. Point your existing OpenAI SDK at https://gateway.futureagi.com/v1. No code change beyond base_url and a virtual key.
  2. 2. Mark Anthropic Claude Opus 5 primary, mirror 20% of traffic to Claude Opus 4.6 in shadow mode. Both responses are logged; only the primary is served to users.
  3. 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
  4. 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.

Full walkthrough on the Agent Command Center page.

FAQ — Anthropic Claude Opus 5 vs Claude Opus 4.6

Which is cheaper, Anthropic Claude Opus 5 or Claude Opus 4.6?

Claude Opus 4.6 is cheaper by roughly 9% on a blended input + output token mix. Input prices are $5.50/M for Anthropic Claude Opus 5 versus $5.00/M for Claude Opus 4.6; output prices are $27.50/M versus $25.00/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.

What is the context window of Anthropic Claude Opus 5 versus Claude Opus 4.6?

Anthropic Claude Opus 5 supports up to 1,000,000 tokens of context. Claude Opus 4.6 supports up to 1,000,000 tokens. Claude Opus 4.6 has the larger window by a factor of 1.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.

Do Anthropic Claude Opus 5 and Claude Opus 4.6 both support tool calling?

Yes — both Anthropic Claude Opus 5 and Claude Opus 4.6 support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.

Which model supports prompt caching for cost reduction?

Both Anthropic Claude Opus 5 and Claude Opus 4.6 support prompt caching. Cached input tokens are typically discounted 50–90% versus uncached input, depending on the provider. For agents with a stable system prompt + retrieval context, the cached pricing tier is the real unit economics number to track.

How do I A/B test Anthropic Claude Opus 5 against Claude Opus 4.6 in production?

Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.