DeepSeek V3 context window support by provider

DeepSeek V3 context window by provider varies more than you’d expect: the model itself handles 128K tokens, but the effective input limit, cache behavior, and per-token cost differ significantly between DeepSeek’s official API, OpenRouter, Together AI, and Fireworks. This head-to-head strips away the marketing so you can wire up the right endpoint without reading four docs sites.

The providers in this comparison

We’re looking at four ways to call DeepSeek V3 today:

• DeepSeek Official (api.deepseek.com) – the source deployment.
• OpenRouter – aggregator that routes to DeepSeek and resellers behind one OpenAI-compatible API.
• Together AI – dedicated GPU inference for open weights, including DeepSeek V3.
• Fireworks AI – high-throughput hosted inference with Fireworks’ own optimization stack.

All four expose an OpenAI-compatible /v1/chat/completions shape, but the details under the hood matter when you’re shipping to production.

Context window capabilities

DeepSeek V3 was trained with a 128K token context. The official API honors that fully: you can send a 128K-token prompt and request up to 8K completion tokens in a single call.

{
  "model": "deepseek-chat",
  "messages": [{"role": "user", "content": "<128k tokens>"}],
  "max_tokens": 8192
}

OpenRouter forwards the same model card, so it advertises 128K as well. In practice, if the upstream provider throttles long contexts, OpenRouter will return a context_length_exceeded error rather than silently truncating.

Together AI and Fireworks both list 128K support for DeepSeek V3. Fireworks sometimes caps single-request output at 4K–8K depending on load, while Together typically allows the full 8K output. Neither implements DeepSeek’s native prefix-cache discount exactly the same way.

The key differentiator is prompt caching. DeepSeek official supports cache_control on messages, letting you mark a stable prefix (system prompt, long document) to be cached for 10 minutes at a 4× cheaper input rate:

client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": long_doc, "cache_control": {"type": "ephemeral"}}
    ]
)

OpenRouter mirrors this for DeepSeek when the underlying provider allows it. Together and Fireworks have their own caching layers but expose them differently (often automatic, not client-directed).

Price and cost model

DeepSeek’s official pricing is public: $0.27 per million input tokens, $1.10 per million output tokens, with cached input at $0.07. That cache discount is the single biggest cost lever for RAG or agent loops.

OpenRouter adds a margin on top of provider cost—typically 5–15%—but gives you one bill and unified metering. If you already use OpenRouter for other models, the convenience is worth the markup.

Together and Fireworks price DeepSeek V3 in the same ballpark but rarely offer the explicit cache-hit discount. You pay for the full input every call unless their automatic caching kicks in, which you can’t always control.

Prices shift; treat the table as mid-2025 ranges, not a quote.

Latency and throughput

Official DeepSeek gives the lowest p50 time-to-first-token for short prompts because there’s no intermediary. Under heavy load, though, their rate limits are strict (sometimes 2K req/min total).

OpenRouter sits in front of DeepSeek and may add 20–50ms of proxy overhead. Its advantage is that when DeepSeek is rate-limited, it can route to a reseller with spare capacity. A gateway like n4n.ai does the same with automatic fallback across 240+ models and honors your cache_control hints, so client code doesn’t change during incidents.

Together and Fireworks run their own fleets. Fireworks often wins on throughput for batched requests; Together is competitive for single-stream latency. Both scale better than official for sustained high QPS if you pay for provisioned throughput.

Ergonomics and SDKs

All four are OpenAI-compatible, so the Python client below works everywhere with a base_url swap:

from openai import OpenAI
client = OpenAI(base_url="https://api.together.xyz/v1", api_key="KEY")
# or "https://api.fireworks.ai/inference/v1", "https://openrouter.ai/api/v1"
resp = client.chat.completions.create(
    model="deepseek-v3",
    messages=[{"role": "user", "content": "Explain transformers"}],
    stream=True
)

DeepSeek official uses model name deepseek-chat for V3; the others use deepseek-v3 or similar. Tool calling is supported on all four, but DeepSeek’s function-calling schema is the reference implementation—others may lag on edge-case parsing.

OpenRouter and gateways give you a single key for many models, which simplifies CI and secret management. Together and Fireworks require separate accounts if you want other open models.

Ecosystem and limits

DeepSeek official is the only place with native V3 weights updates and the longest track record for this specific model. But it’s a single region (Asia-centric), so EU/US users see higher baseline latency.

OpenRouter’s ecosystem is widest: one endpoint, many models, community rankings. Together and Fireworks are part of that ecosystem but also standalone with their own fine-tuning and batch APIs.

Rate limits: official caps concurrent requests aggressively; Together and Fireworks let you buy higher limits; OpenRouter aggregates multiple suppliers so your effective limit is the sum of their inventories.

Which to choose

Cost-sensitive RAG or long-system-prompt agents: Use DeepSeek Official with cache_control on your static prefix. The 4× cache discount dominates total spend.

Multi-model product with DeepSeek as one option: OpenRouter or a similar gateway. You get one billing meter, unified fallback, and no code change when a provider degrades.

High-throughput batch or low-latency US/EU serving: Fireworks or Together. Provision throughput, avoid official rate limits, and accept automatic caching instead of client-directed.

Maximum compatibility with DeepSeek’s own tool-calling quirks: Stick to official until third parties catch up on schema parity.

If you need exactly 128K context with client-controlled caching and you’re already in the DeepSeek ecosystem, the official API is the baseline. Everyone else is trading a few percent of cost or latency for operational flexibility.