What other models does Meta offer?

Meta's Llama family spans a wide range of sizes and architectures, from compact edge-deployable models to large multimodal mixture-of-experts systems. Here are some other models Meta supports:

• Llama 3.1 8B: Llama 3.1 8B is the smallest model in the Llama 3.1 generation, designed for on-device and low-resource inference where cost per request and deployment footprint must be minimized, available under the same open-weight license as the 70B variant

• Llama 3.1 405B: Llama 3.1 405B is the largest dense model in the Llama 3.1 generation, targeting the highest capability tier for tasks where quality takes priority over cost, with pricing notably higher than the 70B variant and suitable for demanding reasoning and code generation workloads

• Llama 3.3 70B: Llama 3.3 70B is a more recent 70B-parameter model that improves on Llama 3.1 70B across instruction-following and reasoning benchmarks, with a 128k-token context window and competitive output pricing, making it the current recommended 70B option for most production use cases

• Llama 4 Scout: Llama 4 Scout is a 109B mixture-of-experts model with 17B active parameters and a 10 million token context window, supporting text and image input at $0.17 per 1M input tokens, positioned as Meta's long-context multimodal option

• Llama 4 Maverick: Llama 4 Maverick is a 402B mixture-of-experts model with 17B active parameters and a 1 million token context window, priced at $0.35 per 1M input tokens, supporting text and image input for higher-quality multimodal workloads

How does Llama 3.1 70B differ from Meta's other models?

Llama 3.1 70B is an earlier-generation 70B model from Meta, now superseded within its own parameter class by Llama 3.3 70B while remaining a stable, widely supported option with broad provider coverage.

• Context window: Llama 3.1 70B supports a 128k-token context window, matching Llama 3.3 70B but well below the 10M token window of Llama 4 Scout or the 1M token window of Llama 4 Maverick

• Generation vs. successors: Llama 3.1 70B predates Llama 3.3 70B and the Llama 4 series. Llama 3.3 70B improves on Llama 3.1 70B's benchmark scores and instruction-following quality while maintaining the same parameter count and cost tier; for new deployments, Llama 3.3 70B is generally the preferred option

• Pricing: Llama 3.1 70B is served by multiple third-party inference providers at competitive rates consistent with other 70B-class open-weight models. Exact pricing varies by provider, but it is meaningfully cheaper than Llama 3.1 405B and the Llama 4 generation at standard per-token rates

• Modality: Llama 3.1 70B is text input and text output only. Llama 3.2 11B/90B and the Llama 4 models support image input; the 3.1 70B does not

• Knowledge cutoff: Llama 3.1 70B has a training knowledge cutoff of early 2024, meaning it lacks awareness of events, APIs, and products released after that date

• Provider breadth: As one of Meta's most widely adopted models, Llama 3.1 70B is available across a larger number of inference providers than newer models, giving teams more options for pricing, latency, and regional availability

Llama 3.1 70B is a practical choice when stability and provider breadth matter, an existing integration is already built around it, or the incremental improvement of Llama 3.3 70B does not justify a migration.

What models should I consider using alongside Llama 3.1 70B?

No single model is optimal for every task. Here are models worth pairing with Llama 3.1 70B depending on what your product needs:

• Llama 3.3 70B (Meta): For tasks that require better instruction following or improved benchmark scores within the same 70B parameter class, Llama 3.3 70B is a direct upgrade with minimal integration changes. Route more demanding requests to Llama 3.3 70B while keeping Llama 3.1 70B active for stable, already-validated workloads

• Claude Sonnet 4 (Anthropic): For complex document analysis, structured data extraction, or use cases where precise formatting and instruction adherence are critical, Claude Sonnet 4 provides strong cross-provider reliability and is a practical escalation target when Llama 3.1 70B misses nuance

• Gemini 2.0 Flash (Google): For high-volume, latency-sensitive inference where throughput per second is the dominant metric and tasks are relatively straightforward, Gemini 2.0 Flash offers fast output speeds at a cost point comparable to 70B-class models

• Llama 4 Scout (Meta): For workloads that involve very long documents, extended conversation threads, or large codebases requiring full context retention, Llama 4 Scout's 10M token context window at $0.17 per 1M input tokens extends what Llama 3.1 70B can handle

• Mistral Large 3 (Mistral AI): For European data-residency requirements or workloads where a 256k-token context window is needed without moving to a Llama 4 model, Mistral Large 3 is a capable open-weight alternative with flexible regional deployment options

What are the challenges of using Llama 3.1 70B in my product?

Like any production LLM, Llama 3.1 70B comes with tradeoffs worth planning for:

• Superseded within its own tier: Llama 3.3 70B improves on Llama 3.1 70B on instruction-following and reasoning benchmarks while matching the 128k context window and 70B parameter count. Teams evaluating new deployments should weigh whether Llama 3.1 70B is the right generation to build on or whether migrating to 3.3 70B is worthwhile

• Provider dependency: Llama 3.1 70B is served by multiple third-party inference providers, each with independent uptime, rate limits, and deprecation timelines. Relying on a single inference provider for this model creates fragility if that provider changes its API contract or has an availability incident

• Cost at scale: Costs compound quickly at high request volumes even at competitive per-token rates. Without active cost governance and selective routing to cheaper models for simpler tasks, a high-throughput pipeline can generate significant monthly spend

• Text-only modality: Llama 3.1 70B does not support image input. Any product roadmap that includes visual features will require introducing a separate multimodal model or migrating to Llama 4, adding routing complexity

• Knowledge cutoff: With a training cutoff of early 2024, the model does not have awareness of events, software releases, or APIs introduced after that date. Applications that surface current information need retrieval-augmented generation or a more recent model checkpoint

Why should I use Merge Gateway to route LLM requests with Llama 3.1 70B and every other model?

Using Llama 3.1 70B through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• One API, every provider: Access Llama 3.1 70B across all inference providers that host it, plus every other major LLM, through a single endpoint and API key. Switch between inference providers or upgrade to Llama 3.3 70B by changing the model string, with no application code changes required

• Intelligent routing and automatic failover: Because Llama 3.1 70B is hosted by multiple third-party providers, Merge can route around any individual provider's outage automatically. Routing policies based on cost, latency, or quality can reduce spend by 40 to 60% without touching your application code

• Cost governance: Set hard or soft project budgets so Llama 3.1 70B 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 Meta's inference layer. Enforce per-project model and region policies without adding that logic to your application

How can I start routing requests to Llama 3.1 70B via Merge Gateway?

Getting Llama 3.1 70B 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 Llama 3.1 70B, the model string is meta/llama-3.1-70b-instruct. 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 Llama 3.1 70B as primary with Llama 3.3 70B as a quality escalation path and a cost-efficient model as a fallback.

Full setup instructions and SDK references are in the Merge Gateway docs.