What other models does Anthropic offer?

Anthropic's Claude family spans multiple capability and price tiers, from lightweight inference models to frontier-grade reasoning systems. Here are some other models Anthropic supports:

• Claude Opus 4.8: Anthropic's current most capable model, built for complex reasoning, long-horizon agentic coding, and high-autonomy workflows, with a 1M token context window and adaptive thinking support

• Claude Sonnet 4.6: The current balanced model offering the best combination of speed and intelligence from Anthropic, priced at $3/$15 per million tokens with extended and adaptive thinking support and a 1M token context window

• Claude Sonnet 4.5: A fast legacy model with extended thinking support, a 200k context window, and $3/$15 per million token pricing, suited to mid-tier tasks where a smaller context window is acceptable

• Claude Haiku 4.5: Anthropic's fastest current model at $1/$5 per million tokens, designed for latency-sensitive, high-throughput applications where the Opus-tier cost structure is not warranted

• Claude 3.5 Haiku: A cost-efficient legacy model at $1/$4 per million tokens that remains in active use for classification, summarization, and structured extraction tasks at scale

How does Claude Opus 4.6 differ from Anthropic's other models?

Claude Opus 4.6 is a high-intelligence non-reasoning model occupying the upper tier of Anthropic's legacy lineup, with a 1M token context window that sets it apart from the 200k-context Sonnet and Haiku models.

• Context window: Claude Opus 4.6 provides a 1M token context window, matching the current flagship Claude Opus 4.8 and Claude Sonnet 4.6, and significantly larger than the 200k context on Claude Sonnet 4.5 and Haiku-class models. This makes it well suited to pipelines that process entire codebases, long legal documents, or extended multi-turn conversation histories

• Pricing: At $5/$25 per million input/output tokens (as of 06/01/2026), Claude Opus 4.6 is priced at the Opus tier, roughly five times the input cost of Claude Sonnet 4.5 and sixteen times the input cost of Claude 3.5 Haiku. This reflects its position as a premium model and makes cost-per-request management important at volume

• Intelligence ranking: On the Artificial Analysis Intelligence Index, Claude Opus 4.6 ranks #2 out of 71 non-reasoning models evaluated (as of 06/01/2026), placing it among the top-performing models available for tasks that do not require chain-of-thought reasoning steps

• Speed: Claude Opus 4.6 generates approximately 41.1 tokens per second with a time to first token of 1.33 seconds (as of 06/01/2026), which is below the category median of 58.3 tokens/s. Applications with strict latency budgets should weigh this against the intelligence benefit

• Extended and adaptive thinking: Claude Opus 4.6 supports both extended thinking and adaptive thinking, giving it reasoning flexibility that Claude Sonnet 4.5 lacks on the adaptive side

Claude Opus 4.6 is the right choice when you need near-frontier intelligence, a 1M token context window, and extended or adaptive thinking, but the request volume or cost structure makes the current flagship Opus 4.8 harder to justify.

What models should I consider using alongside Claude Opus 4.6?

No single model is optimal for every task. Here are models worth pairing with Claude Opus 4.6 depending on what your product needs:

• Claude Haiku 4.5 for the high-volume, low-complexity tier of your workload. Routing classification, triage, and simple extraction requests to Haiku at $1/$5 per million tokens frees Claude Opus 4.6 budget for tasks where the intelligence gap matters

• Claude Opus 4.8 when a task requires adaptive reasoning under uncertainty or involves long-horizon agentic execution with tool use. Claude Opus 4.8 is Anthropic's current ceiling and may outperform 4.6 on the hardest multi-step tasks

• GPT-4o as a cross-provider fallback or A/B comparison target. When Anthropic experiences elevated latency or rate limits, routing to GPT-4o maintains service continuity without degrading quality significantly for most task types

• Gemini 2.5 Pro for tasks that benefit from Google's large context window and strong performance on structured data and multimodal inputs, particularly when the same pipeline needs to process both text and images at scale

• Mistral Large for European data residency requirements or workloads where EU-based inference is a compliance constraint, where Claude Opus 4.6 via Anthropic's API may not meet regional processing rules

What are the challenges of using Claude Opus 4.6 in my product?

Like any production LLM, Claude Opus 4.6 comes with tradeoffs worth planning for:

• Provider dependency: Relying exclusively on Anthropic for Opus-tier requests means any API outage, rate limit event, or service degradation directly affects your highest-priority tasks, which are often the ones least tolerant of latency spikes

• Cost at scale: At $25 per million output tokens (as of 06/01/2026), output costs from Claude Opus 4.6 compound quickly. A pipeline generating 50 million output tokens per month from this model faces $1,250 in output spend before any other infrastructure cost

• Below-average generation speed: At 41.1 tokens per second (as of 06/01/2026), Claude Opus 4.6 is slower than the category median of 58.3 tokens/s. Real-time applications or streaming UIs that surface responses incrementally will feel this gap compared to faster models in the same intelligence tier

• Legacy status: Claude Opus 4.6 is listed as a legacy model in Anthropic's documentation. It will not receive new capability updates, and migration to a current model is recommended before the model reaches end-of-life

• Verbose output behavior: According to Artificial Analysis evaluation data, Claude Opus 4.6 generated approximately 11 million output tokens during Intelligence Index testing, classified as "somewhat verbose" relative to peers. Applications with strict output length budgets should tune prompts accordingly or monitor token usage per response

Why should I use Merge Gateway to route LLM requests with Claude Opus 4.6 and every other model?

Using Claude Opus 4.6 through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• One API, every provider: Access Claude Opus 4.6 and every other major LLM through a single endpoint and API key. Change providers by swapping the model string, no application code changes required

• Intelligent routing and automatic failover: Merge routes around Anthropic outages automatically. Routing policies based on cost, latency, or quality can reduce spend by 40–60% without touching your application code

• Cost governance: Set hard or soft project budgets so Claude Opus 4.6 spend stays within plan. Every request is attributed to a model, project, and tag in a unified billing dashboard across all providers

• Build Your Own Router: Define what "best" means for your traffic by selecting from curated ML benchmarks or adding your own eval scores. The router scores each available model against your weights and picks the winner per request, with a plain-language explanation of every decision

• Security and compliance controls: Apply DLP rules and prompt injection protection before every request reaches Anthropic. Enforce per-project model and region policies without adding that logic to your application

How can I start using Merge Gateway to route requests with Claude Opus 4.6?

Getting Claude Opus 4.6 running through Merge Gateway takes a few minutes:

1. Create an account and get your API key from the dashboard.

2. Install the Merge Gateway SDK: run pip install merge-gateway-sdk (Python) or npm install merge-gateway-sdk (Node). Alternatively, if you're already using the OpenAI SDK, set base_url = "https://api-gateway.merge.dev/v1/openai" and your existing code works as-is.

3. Make your first request using the provider/model format. For Claude Opus 4.6, the model string is anthropic/claude-opus-4-6. Swap the model string to route to any other provider without changing anything else.

4. Configure a routing policy in the dashboard to set failover behavior, cost limits, and optimization strategy. Your first policy can be as simple as naming Claude Opus 4.6 as primary with one fallback.

You can find full setup instructions and SDK references in the Merge Gateway docs.