Anthropic Claude 3.7 Sonnet 20240620 v1.0 vs Claude Opus 4.6 Default
Anthropic Claude 3.7 Sonnet 20240620 v1.0 (Amazon Bedrock, 200,000-token context) versus Claude Opus 4.6 Default (Google Vertex AI, 1,000,000-token context). Anthropic Claude 3.7 Sonnet 20240620 v1.0 is cheaper by 28% 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 3.7 Sonnet 20240620 v1.0 vs Claude Opus 4.6 Default
Anthropic Claude 3.7 Sonnet 20240620 v1.0 and Claude Opus 4.6 Default target overlapping workloads but differ sharply on economics. Anthropic Claude 3.7 Sonnet 20240620 v1.0 runs roughly 28% cheaper on a blended input-plus-output token mix, which translates to approximately $8,400 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
Claude Opus 4.6 Default ships a 1,000,000-token context window, 5.0x larger than Anthropic Claude 3.7 Sonnet 20240620 v1.0's 200,000 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 200,000 tokens, the extra context on Claude Opus 4.6 Default is insurance you may never use — and Anthropic Claude 3.7 Sonnet 20240620 v1.0 may win on other axes.
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.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: anthropic-claude-3-7-sonnet-20240620-v1-0
provider: bedrock
fallback:
model: claude-opus-4-6-default
provider: vertex-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Anthropic Claude 3.7 Sonnet 20240620 v1.0 | Claude Opus 4.6 Default | |
|---|---|---|
| Input price | $3.60/M | $5.00/M |
| Output price | $18.00/M | $25.00/M |
| Context window | 200,000 | 1,000,000 |
| Max output | 8,192 | 128,000 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | Aug 6, 2026 | Aug 6, 2026 |
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 3.7 Sonnet 20240620 v1.0 | Claude Opus 4.6 Default | Delta |
|---|---|---|---|
| Startup 10K requests/day | $2,160 /mo | $3,000 /mo | $840/mo |
| Mid-market 100K requests/day | $21,600 /mo | $30,000 /mo | $8,400/mo |
| Enterprise 1M requests/day | $216,000 /mo | $300,000 /mo | $84,000/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.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
You're cost-sensitive at scale — Anthropic Claude 3.7 Sonnet 20240620 v1.0 runs ~28% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — Claude Opus 4.6 Default fits 1,000,000 tokens versus the other model's 200,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 400% when moving from Anthropic Claude 3.7 Sonnet 20240620 v1.0 (200,000) to Claude Opus 4.6 Default (1,000,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 8,192 on Anthropic Claude 3.7 Sonnet 20240620 v1.0 vs 128,000 on Claude Opus 4.6 Default. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Provider changes from Amazon Bedrock to Google Vertex AI. 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 3.7 Sonnet 20240620 v1.0 vs Claude Opus 4.6 Default 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. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Anthropic Claude 3.7 Sonnet 20240620 v1.0 primary, mirror 20% of traffic to Claude Opus 4.6 Default in shadow mode. Both responses are logged; only the primary is served to users.
- 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. 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 3.7 Sonnet 20240620 v1.0 vs Claude Opus 4.6 Default
Which is cheaper, Anthropic Claude 3.7 Sonnet 20240620 v1.0 or Claude Opus 4.6 Default? ▾
Anthropic Claude 3.7 Sonnet 20240620 v1.0 is cheaper by roughly 28% on a blended input + output token mix. Input prices are $3.60/M for Anthropic Claude 3.7 Sonnet 20240620 v1.0 versus $5.00/M for Claude Opus 4.6 Default; output prices are $18.00/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 3.7 Sonnet 20240620 v1.0 versus Claude Opus 4.6 Default? ▾
Anthropic Claude 3.7 Sonnet 20240620 v1.0 supports up to 200,000 tokens of context. Claude Opus 4.6 Default supports up to 1,000,000 tokens. Claude Opus 4.6 Default has the larger window by a factor of 5.0x, 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 3.7 Sonnet 20240620 v1.0 and Claude Opus 4.6 Default both support tool calling? ▾
Yes — both Anthropic Claude 3.7 Sonnet 20240620 v1.0 and Claude Opus 4.6 Default 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 3.7 Sonnet 20240620 v1.0 and Claude Opus 4.6 Default 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.
When should I choose Anthropic Claude 3.7 Sonnet 20240620 v1.0 over Claude Opus 4.6 Default? ▾
You're cost-sensitive at scale — Anthropic Claude 3.7 Sonnet 20240620 v1.0 runs ~28% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
When should I choose Claude Opus 4.6 Default over Anthropic Claude 3.7 Sonnet 20240620 v1.0? ▾
Your workload needs long context — Claude Opus 4.6 Default fits 1,000,000 tokens versus the other model's 200,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
How do I A/B test Anthropic Claude 3.7 Sonnet 20240620 v1.0 against Claude Opus 4.6 Default 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.