Anthropic Claude 3.7 Sonnet 20240620 v1.0 vs Jp Anthropic Claude Sonnet 4.5 20250929 v1.0
Anthropic Claude 3.7 Sonnet 20240620 v1.0 (Amazon Bedrock, 200,000-token context) versus Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 (Amazon Bedrock, 200,000-token context). Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 is cheaper by 8% 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 Jp Anthropic Claude Sonnet 4.5 20250929 v1.0
Anthropic Claude 3.7 Sonnet 20240620 v1.0 and Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 are priced within 8% 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.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: jp-anthropic-claude-sonnet-4-5-20250929-v1-0
provider: bedrock
fallback:
model: anthropic-claude-3-7-sonnet-20240620-v1-0
provider: bedrock
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Anthropic Claude 3.7 Sonnet 20240620 v1.0 | Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 | |
|---|---|---|
| Input price | $3.60/M | $3.30/M |
| Output price | $18.00/M | $16.50/M |
| Context window | 200,000 | 200,000 |
| Max output | 8,192 | 64,000 |
| 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 | Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $2,160 /mo | $1,980 /mo | $180/mo |
| Mid-market 100K requests/day | $21,600 /mo | $19,800 /mo | $1,800/mo |
| Enterprise 1M requests/day | $216,000 /mo | $198,000 /mo | $18,000/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.
- Max output tokens differ: 8,192 on Anthropic Claude 3.7 Sonnet 20240620 v1.0 vs 64,000 on Jp Anthropic Claude Sonnet 4.5 20250929 v1.0. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
How to A/B test Anthropic Claude 3.7 Sonnet 20240620 v1.0 vs Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 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 Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 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 Jp Anthropic Claude Sonnet 4.5 20250929 v1.0
Which is cheaper, Anthropic Claude 3.7 Sonnet 20240620 v1.0 or Jp Anthropic Claude Sonnet 4.5 20250929 v1.0? ▾
Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 is cheaper by roughly 8% on a blended input + output token mix. Input prices are $3.60/M for Anthropic Claude 3.7 Sonnet 20240620 v1.0 versus $3.30/M for Jp Anthropic Claude Sonnet 4.5 20250929 v1.0; output prices are $18.00/M versus $16.50/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 Jp Anthropic Claude Sonnet 4.5 20250929 v1.0? ▾
Anthropic Claude 3.7 Sonnet 20240620 v1.0 supports up to 200,000 tokens of context. Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 supports up to 200,000 tokens. Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 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 3.7 Sonnet 20240620 v1.0 and Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 both support tool calling? ▾
Yes — both Anthropic Claude 3.7 Sonnet 20240620 v1.0 and Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 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 Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 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 3.7 Sonnet 20240620 v1.0 against Jp Anthropic Claude Sonnet 4.5 20250929 v1.0 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.