OpenRouter vs TogetherAI: Choosing the Right AI API (2025)

This is evergreen content. As of November 2025, OpenRouter and TogetherAI are two of the most talked‑about AI API providers, but they solve slightly different problems. One is a broad “any model, any provider” gateway; the other is a specialized, high‑performance host for open‑source and reasoning models. This guide compares OpenRouter vs TogetherAI on pricing, latency, and model availability so you can decide which is right for your budget and performance targets, and whether you’re better off with an API aggregator or a specialized provider.

What OpenRouter and TogetherAI actually are

OpenRouter in a nutshell

OpenRouter is a unified LLM API gateway. Through a single OpenAI‑compatible endpoint, you can access hundreds of models from dozens of upstream providers (OpenAI, Anthropic, Google, Meta Llama, xAI, DeepSeek, Mistral, etc.). As of November 2025:

• Catalog: 575+ models listed in the OpenRouter models catalog
• Plans: Free, Pay‑as‑you‑go, and Enterprise (pricing page, updated 2025)
• Platform fee: 5.5% fee on Pay‑as‑you‑go usage (no markup on base model prices)
• Free tier: 25+ free models, 50 requests/day (20 RPM) on free plan
• API: OpenAI‑compatible /chat/completions with extras like provider routing, budgets, and prompt caching

Think of OpenRouter as a meta‑provider: it routes to many underlying vendors and models while giving you one API surface, shared logging, and cost controls.

TogetherAI in a nutshell

TogetherAI is an “AI acceleration cloud” focused on running and fine‑tuning open‑source and specialized models at high performance. It offers:

• Serverless Inference API for 200+ models (model library)
• Dedicated Endpoints with single‑tenant GPUs and guaranteed performance
• Fine‑tuning platform (LoRA and full FT) and GPU clusters (Instant and Reserved)
• Pricing: transparent per‑token/per‑minute pricing by model on the TogetherAI pricing page (last updated November 2025)
• API: OpenAI‑compatible chat completions (docs) plus batch, code execution, and evaluations

TogetherAI behaves more like a specialized, high‑throughput inference and training provider than a generic traffic router.

Pricing: OpenRouter vs TogetherAI for LLM API cost

Both platforms use per‑token billing for text models, but their pricing philosophies differ. OpenRouter mirrors provider prices plus a platform fee; TogetherAI prices its own infrastructure and models directly.

OpenRouter pricing model

• Pay‑as‑you‑go platform fee: 5.5% on top of underlying model spend (from OpenRouter pricing)
• Model prices: match each provider’s listed price (no markup). For each model card (e.g. GPT‑4.1, Llama 3.1 70B Instruct), you see per‑million‑token input/output rates.
• Free models: Many models with :free suffix (e.g. DeepSeek V3 and R1 variants, smaller Llama, etc.), but with stricter rate limits.
• BYOK (Bring Your Own Key): You can route requests using your own vendor keys; Pay‑as‑you‑go includes 1M free BYOK requests/month, then 5% fee after (per pricing page).

Practically, you pay:

• Raw token cost: whatever OpenAI, Anthropic, Meta, etc. charge for the model
• + 5.5% OpenRouter fee (or lower with enterprise discounts)

TogetherAI pricing model

TogetherAI exposes detailed pricing per model on its pricing page. For Text & Vision models (as of November 2025):

• Llama 4 Maverick: $0.27 / 1M input tokens, $0.85 / 1M output tokens
• Llama 3.1 8B Instruct Turbo: $0.18 / 1M tokens (same for input & output)
• DeepSeek‑R1: $3.00 / 1M input, $7.00 / 1M output
• gpt‑oss‑120B: $0.15 / 1M input, $0.60 / 1M output
• Qwen2.5 7B Instruct Turbo: $0.30 / 1M tokens

There is no additional platform percentage fee advertised; your bill is simply the per‑token rate times usage, plus any fine‑tuning or GPU cluster costs if you use those.

Head‑to‑head cost comparison

Since OpenRouter often proxies to TogetherAI for some models, an apples‑to‑apples LLM API cost comparison depends on whether you care about that extra few percent and routing convenience.

Cost takeaway: If you’re standardizing on TogetherAI‑hosted models (e.g. Llama 4, DeepSeek, Qwen) and don’t need multi‑provider routing, TogetherAI will usually be slightly cheaper at scale because there is no platform percentage fee. If you want one endpoint to reach Anthropic + OpenAI + Meta + DeepSeek + others and you value routing/fallback, OpenRouter’s 5.5% overhead is typically worth it.

Latency and reliability: which is faster?

Latency is shaped by:

• Model size and architecture
• Inference stack and GPUs
• Network distance between your app and the provider
• Any extra routing layer

OpenRouter latency characteristics

OpenRouter introduces a thin gateway layer between you and the actual model host. Public benchmarks and reviews in 2025 (e.g. AI gateway comparisons on AIMultiple and Helicone) generally show:

• Extra overhead: ~20–30 ms typical added latency compared to calling the underlying provider directly
• Routing impact: If you use auto‑routing or fallbacks, OpenRouter may pick the “best” endpoint per request, but can also incur minor discovery overhead
• Region routing: Enterprise and PAYG can pin regions to keep latency predictable

For many web apps, that 20–30 ms is negligible relative to overall generation time for a 10–30 token/s model. For ultra‑low‑latency and streaming‑critical use cases (real‑time voice, trading tools, gaming), it can matter.

TogetherAI latency characteristics

TogetherAI runs its own optimized inference stack, with a 2025 focus on:

• High throughput serverless endpoints (e.g. DeepSeek‑R1 throughput variants, Qwen3‑235B FP8 throughput models)
• Dedicated endpoints on H100/H200/B200 GPUs with predictable and often lower tail latencies
• Batch API offering up to 50% lower cost and improved effective throughput for background workloads

Industry benchmarks and customer stories (Hedra, Vercept, etc.) cited by TogetherAI routinely report top‑tier latency for open‑source models, often beating self‑hosted or generic gateways.

Latency takeaway:

• If you need the fastest possible inference on specific open‑source models at scale, TogetherAI (especially with dedicated endpoints) generally wins.
• If you need reliability and fallbacks across many providers, OpenRouter’s routing is more valuable than shaving 20 ms from p50 latency.

Model availability and ecosystems

OpenRouter model coverage

The OpenRouter models page lists 575 models as of November 2025. Highlights include:

• Closed and semi‑closed models: OpenAI GPT‑4.1 family, GPT‑4o (2024‑11‑20 releases), Anthropic Claude 3.5 & later variants, Google Gemini, etc.
• Open models: Meta Llama 3.1/3.3 and Llama 4, DeepSeek V3/R1, Qwen families, Mistral models, GLM, Kimi, etc.
• Specialized models: embeddings, rerankers, moderation, safety models (e.g. Llama Guard), code‑focused LLMs, roleplay/chat‑tuned models.
• Multiple providers per model: the same model (e.g. Llama 3.1 70B Instruct) can be hosted by multiple providers; OpenRouter routes between them.

OpenRouter’s unique strength is mixing closed commercial models (like GPT‑4.1, Claude) and open ecosystems behind one API, plus per‑model privacy policies and provider data controls.

TogetherAI model coverage

TogetherAI’s model library focuses on top open‑source and partner models, for example:

• Meta Llama 3.x and Llama 4: Llama 3.1/3.2/3.3 and Llama 4 Maverick & Scout
• DeepSeek: DeepSeek V3, V3.1, R1, throughput and experimental variants
• Qwen: Qwen2.5, Qwen3 families, coder and VL models
• Reasoning and agentic models: gpt‑oss‑120B, Kimi K2, GLM‑4.6, MiniMax M1, Apriel Thinker, Cogito series
• Multimodal: FLUX.1 and FLUX1.1 image models, Google Imagen 4, Google Veo 3, Gemini Flash Image 2.5
• Audio & ASR: Cartesia Sonic‑2, Whisper Large v3
• Embeddings & rerankers: BGE, M2‑BERT, GTE ModernBERT, mxbai‑rerank, LlamaRank

TogetherAI does not give you proprietary GPT‑4.1 or Claude via their closed APIs; instead it offers OpenAI’s “gpt‑oss” open‑weight line and similar high‑end open models, plus strong training and GPU services.

Model availability takeaway:

• If you need closed models like GPT‑4.1, GPT‑4o, Claude, Gemini alongside open‑source, OpenRouter is your only choice here.
• If your roadmap is primarily open‑source + reasoning models and you care about performance and fine‑tuning, TogetherAI’s curated model set is usually enough and highly optimized.

Developer experience, routing, and controls

OpenRouter: multi‑provider control plane

Key DX features from the OpenRouter docs and pricing pages:

• OpenAI‑compatible API: swap the base URL, adjust model names, and most OpenAI client libraries work out of the box.
• Model routing and fallbacks: automatically fail over to alternate providers, only charging for successful runs (“Zero Completion Insurance”).
• Budgets & spend controls: per‑key limits, environment separation, alerts, and activity logs.
• Prompt caching: reduce cost for repeated prompts.
• Data policy‑based routing: choose models/providers that meet your logging and data retention requirements on a per‑request basis.

OpenRouter shines as a control plane across many vendors: ideal if you’re experimenting, A/B testing models, or running multi‑tenant AI products that might burst across providers.

TogetherAI: inference + training platform

From the TogetherAI docs and product pages, the emphasis is on a full stack:

• Serverless inference: OpenAI‑compatible chat completions, images, audio, video, vision, embeddings, rerank.
• Fine‑tuning: LoRA and full fine‑tuning with clear per‑token pricing and support for large models like Llama 4, DeepSeek, Qwen3.
• GPU clusters: Instant and Reserved H100/H200/B200 clusters for custom training, with Kubernetes or Slurm and high‑speed networking.
• Code Sandbox & Code Interpreter: co‑locate code execution with models for agents and dev workflows.
• Evaluations & batch API: built‑in evals and discounted batch inference.

TogetherAI is a better fit if you want a single vendor for inference + training + GPU infra and expect to grow into custom models or large‑scale training.

How to choose: API aggregator vs specialized provider

Choosing between OpenRouter vs TogetherAI comes down to your workload patterns and constraints. Use this decision framework:

Choose OpenRouter if…

• You need multiple ecosystems: GPT‑4.1, Claude 3.5+, Gemini, Llama 4, DeepSeek, Qwen, and more behind a single key.
• You iterate on models frequently: you’re testing which LLM is best for specific tasks and want to swap models without rewriting integration code.
• You want vendor redundancy: automatic fallbacks across providers and regions matter more than a few ms of latency.
• You care about per‑provider data policies: routing based on logging/retention policies is a requirement.
• Your spend is moderate: the 5.5% platform fee is acceptable for the operational simplicity and flexibility you gain.

Choose TogetherAI if…

• You’re all‑in on open‑source and reasoning models: DeepSeek, Llama, Qwen, GLM, Kimi, etc. are your primary workhorses.
• Latency and throughput are critical: you need optimized endpoints or dedicated GPUs with tight SLOs.
• You plan to fine‑tune or train: having inference, fine‑tuning, and GPU clusters under one roof simplifies your stack.
• You want to avoid platform fees: paying only per token (or per GPU hour) is better for your economics at scale.
• You’re building agents or dev tools: code execution, evals, and batch APIs alongside models are attractive.

When to use both together

Many 2025 architectures combine them:

• Use TogetherAI as the primary inference and training platform for open‑source models and cost‑sensitive workloads.
• Use OpenRouter as a “meta layer” to reach closed models (GPT‑4.1, Claude, Gemini) and to route to TogetherAI or other providers as an additional backend when needed.

Practical next steps

1. Estimate your token usage: For each use case, estimate monthly input/output tokens. Plug those numbers into:
   • OpenRouter model cards + 5.5% platform fee
   • TogetherAI pricing tables
2. Define your model set: Decide whether you truly need GPT‑4.1 / Claude / Gemini. If yes, OpenRouter (or direct vendors) are required; if not, TogetherAI may suffice.
3. Run latency tests: Implement a minimal OpenAI‑compatible client and A/B requests between OpenRouter and TogetherAI for your target models and regions.
4. Plan for growth: If you foresee custom fine‑tuning or on‑prem‑like GPU usage, factor TogetherAI’s GPU clusters and FT platform into your roadmap.
5. Start small, keep options open: Use OpenRouter to quickly experiment across many models, then standardize high‑volume workloads on TogetherAI or specific providers once you know what works.

As of November 2025, both OpenRouter and TogetherAI are mature, actively updated platforms. Your best choice depends less on raw “who is better” and more on whether you want a multi‑provider routing layer (OpenRouter) or a specialized, high‑performance home for open‑source and reasoning models (TogetherAI). For many teams, starting with OpenRouter for breadth and standardizing champions on TogetherAI for depth and cost is the most resilient strategy.