Claude 4 Opus vs Jp Anthropic Claude Sonnet 4.6
Claude 4 Opus (Snowflake Cortex, 200,000-token context) versus Jp Anthropic Claude Sonnet 4.6 (Amazon Bedrock, 1,000,000-token context). Jp Anthropic Claude Sonnet 4.6 is cheaper by 34% on a blended token mix. Jp Anthropic Claude Sonnet 4.6 uniquely supports pdf input. 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 — Claude 4 Opus vs Jp Anthropic Claude Sonnet 4.6
Claude 4 Opus and Jp Anthropic Claude Sonnet 4.6 target overlapping workloads but differ sharply on economics. Jp Anthropic Claude Sonnet 4.6 runs roughly 34% cheaper on a blended input-plus-output token mix, which translates to approximately $10,200 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.
Jp Anthropic Claude Sonnet 4.6 ships a 1,000,000-token context window, 5.0x larger than Claude 4 Opus'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 Jp Anthropic Claude Sonnet 4.6 is insurance you may never use — and Claude 4 Opus may win on other axes.
On capability surface area, the models diverge: Jp Anthropic Claude Sonnet 4.6 supports pdf input where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
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-4-6
provider: bedrock
fallback:
model: claude-4-opus
provider: snowflake-cortex
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Claude 4 Opus | Jp Anthropic Claude Sonnet 4.6 | |
|---|---|---|
| Input price | $5.00/M | $3.30/M |
| Output price | $25.00/M | $16.50/M |
| Context window | 200,000 | 1,000,000 |
| Max output | 16,384 | 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 | Claude 4 Opus | Jp Anthropic Claude Sonnet 4.6 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $3,000 /mo | $1,980 /mo | $1,020/mo |
| Mid-market 100K requests/day | $30,000 /mo | $19,800 /mo | $10,200/mo |
| Enterprise 1M requests/day | $300,000 /mo | $198,000 /mo | $102,000/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 — Jp Anthropic Claude Sonnet 4.6 runs ~34% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — Jp Anthropic Claude Sonnet 4.6 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.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Claude 4 Opus, switching to Jp Anthropic Claude Sonnet 4.6 means re-architecting that path (and vice versa).
- • PDF input
Capabilities both share (6)
- ✓ Function calling
- ✓ Vision input
- ✓ Streaming
- ✓ Structured output (JSON schema)
- ✓ Prompt caching
- ✓ Native reasoning mode
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 Claude 4 Opus (200,000) to Jp Anthropic Claude Sonnet 4.6 (1,000,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 16,384 on Claude 4 Opus vs 64,000 on Jp Anthropic Claude Sonnet 4.6. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Jp Anthropic Claude Sonnet 4.6 has capabilities Claude 4 Opus lacks: PDF input. Worth wiring through the agent design before commit.
- Provider changes from Snowflake Cortex to Amazon Bedrock. 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 Claude 4 Opus vs Jp Anthropic Claude Sonnet 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. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Claude 4 Opus primary, mirror 20% of traffic to Jp Anthropic Claude Sonnet 4.6 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 — Claude 4 Opus vs Jp Anthropic Claude Sonnet 4.6
Which is cheaper, Claude 4 Opus or Jp Anthropic Claude Sonnet 4.6? ▾
Jp Anthropic Claude Sonnet 4.6 is cheaper by roughly 34% on a blended input + output token mix. Input prices are $5.00/M for Claude 4 Opus versus $3.30/M for Jp Anthropic Claude Sonnet 4.6; output prices are $25.00/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 Claude 4 Opus versus Jp Anthropic Claude Sonnet 4.6? ▾
Claude 4 Opus supports up to 200,000 tokens of context. Jp Anthropic Claude Sonnet 4.6 supports up to 1,000,000 tokens. Jp Anthropic Claude Sonnet 4.6 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 Claude 4 Opus and Jp Anthropic Claude Sonnet 4.6 both support tool calling? ▾
Yes — both Claude 4 Opus and Jp Anthropic Claude Sonnet 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 Claude 4 Opus and Jp Anthropic Claude Sonnet 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.
When should I choose Claude 4 Opus over Jp Anthropic Claude Sonnet 4.6? ▾
On the data this page surfaces, Claude 4 Opus is the right pick when Jp Anthropic Claude Sonnet 4.6's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
When should I choose Jp Anthropic Claude Sonnet 4.6 over Claude 4 Opus? ▾
You're cost-sensitive at scale — Jp Anthropic Claude Sonnet 4.6 runs ~34% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume. Your workload needs long context — Jp Anthropic Claude Sonnet 4.6 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 Claude 4 Opus against Jp Anthropic Claude Sonnet 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.