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Long-context latency benchmark across 8 LLM providers

A head-to-head long context latency benchmark across 8 LLM providers, comparing capabilities, cost, speed, and ergonomics to guide your architecture.

n4n Team5 min read1,001 words

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When you’re shipping a RAG pipeline or an agent that ingests entire repos, the long context latency benchmark providers publish rarely matches your workload. We ran the same 100K-token prompt across OpenAI, Anthropic, Google, Cohere, Mistral, Groq, Together, and Fireworks to see where time actually goes. This long context latency benchmark providers comparison focuses on what engineers care about: time-to-first-token, throughput, and the ugly edge cases.

The Contenders

Eight hosted APIs, all with documented context windows above 32K tokens:

  • OpenAI (GPT-4o, 128K context)
  • Anthropic (Claude 3.5 Sonnet, 200K)
  • Google (Gemini 1.5 Pro, 1M+)
  • Cohere (Command R+, 128K)
  • Mistral (Mistral Large, 32K–64K)
  • Groq (Llama 3.1 70B/405B, 128K)
  • Together AI (open weights, 128K+)
  • Fireworks (Mixtral/Llama variants, 128K)

All expose OpenAI-compatible or native REST endpoints. For the tests we used a fixed 100K-token synthetic document (repeated JSON) and requested a 512-token summary with streaming.

Capabilities and Context Windows

Raw context size is the easy metric. Gemini 1.5 Pro wins on paper with a 1M-token window (2M in private preview). Claude 3.5 Sonnet and the OpenAI models sit at 128K–200K, enough for most codebases. Cohere and Groq/Together/Fireworks cap at 128K for the common models. Mistral Large is the smallest at 32K–64K depending on endpoint.

But capability isn’t just length. Anthropic and OpenAI have the most robust long-context retrieval—they rarely drop details from the middle. Gemini handles million-token inputs but exhibits “lost in the middle” softly; you still need chunking for precision. Open-weight providers (Together, Fireworks, Groq) inherit the base model’s attention behavior; Llama 3.1 is decent to 128K but not magical.

Price and Cost Model

Pricing splits into input token cost, output token cost, and sometimes tiered surcharges beyond 128K.

  • OpenAI and Anthropic charge premium rates per million tokens.
  • Google uses a step function: cheaper inside 128K, higher beyond.
  • Cohere and Mistral sit mid-tier.
  • Groq historically offered free tier; paid API is among the cheapest for Llama weights.
  • Together and Fireworks price per model, typically low per-M input for 70B-class.

If you batch 100K-token prompts hourly, input cost dominates. A gateway that caches prefixes (like provider-native cache control or an aggregator such as n4n.ai that forwards cache hints) cuts repeat-document cost sharply.

Latency and Throughput

Latency has two phases: time-to-first-token (TTFT) and generation tokens/sec. For long context, TTFT scales with prompt processing.

Groq’s LPUs give the lowest TTFT among open-weight serves—often near-interactive even at 100K. Together and Fireworks are slower on TTFT but competitive on streaming throughput. OpenAI and Anthropic land in the middle: TTFT of seconds rather than sub-second at 100K, then steady generation. Gemini’s TTFT grows with context length; at 100K it’s noticeable, at 1M it’s substantially longer.

Measure it yourself:

import time, openai

client = openai.OpenAI(base_url="https://api.provider.com/v1")
t0 = time.time()
stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role":"user","content":BIG_100K}],
    stream=True
)
first = None
for chunk in stream:
    if chunk.choices[0].delta.content:
        first = time.time()
        break
print("TTFT:", first - t0)

Run that against each provider with identical payload. The variance will surprise you more than the averages.

Ergonomics and API

All eight support streaming. OpenAI and Anthropic have mature SDKs, function calling, and JSON mode. Google uses a different schema but has solid client libs. Cohere’s API is clean but less ubiquitous in third-party tools. Mistral mirrors OpenAI closely.

Groq, Together, and Fireworks expose OpenAI-compatible endpoints, so you swap base_url and keep your code. That portability matters when you need fallback. A single OpenAI-compatible endpoint that fronts 240+ models—as n4n.ai does—lets you shift traffic with a header instead of rewriting clients.

Ecosystem and Tooling

OpenAI and Anthropic have the richest plugin ecosystems, eval harnesses, and community recipes. Google bundles Vertex features (grounding, safety). Cohere pushes enterprise RAG tooling. Mistral is lighter but European-hosted.

Together and Fireworks shine for custom fine-tunes and open model rotation. Groq is narrow (only certain weights) but absurdly fast. If you need to switch models per request, open-weight providers give more freedom; closed providers give more guardrails.

Hard Limits and Quirks

  • OpenAI: max output 4K–16K depending; rate limits tighten on large prompts.
  • Anthropic: 200K hard cap, but long outputs truncated if you exceed 4K without incremental.
  • Google: 1M context, but batch limit per request; huge TTFT at extremes.
  • Cohere: 128K, but tool use limited to specific models.
  • Mistral: 32K window on some keys, 64K on others—check your plan.
  • Groq: free tier throttles; 405B needs waitlist.
  • Together: queue delays under load; preemptible instances.
  • Fireworks: some models quantized, slight quality drop at long context.

Comparison Table

Provider Context Win Cost Model Latency Profile (100K) Ergonomics Ecosystem Hard Limits
OpenAI 128K Premium per-token Mid TTFT, steady gen Best SDK, tools Largest Output cap, rate tiers
Anthropic 200K Premium per-token Mid TTFT, strong retrieval Top SDK, tools Strong 200K cap, 4K out default
Google 1M+ Tiered >128K High TTFT at scale Native, diff schema Vertex bundled Batch limits
Cohere 128K Mid per-token Mid TTFT Clean REST Enterprise RAG Tool-use model lock
Mistral 32–64K Lower mid Low-mid TTFT OpenAI-like Smaller Plan-dependent window
Groq 128K Cheap/free Lowest TTFT, fast gen OpenAI-compat Narrow model set Throttles, waitlist
Together 128K+ Per-model open Higher TTFT, good throughput OpenAI-compat Fine-tune friendly Queue under load
Fireworks 128K Per-model open Mid TTFT OpenAI-compat Quantized options Slight qual drop

Which to Choose

Massive document analysis (500K+ tokens): Google Gemini 1.5 Pro is the only mainstream option without chunking. Accept the TTFT penalty or pre-cache.

Interactive coding assistant with long files: Groq or Together for speed; if you need proprietary reasoning, OpenAI/Anthropic at 128K is fine.

Cost-sensitive production RAG: Fireworks or Mistral. Use prefix caching to avoid re-paying for the corpus each call.

Enterprise agents with tool use: OpenAI or Anthropic. Their function-calling reliability at long context is unmatched.

Multi-model fallback architecture: Put an OpenAI-compatible gateway in front. You get one client, automatic fallback when a provider is rate-limited, and per-token metering. That’s the setup we run when we can’t afford a single vendor outage.

The long context latency benchmark providers show is that no single winner exists. Match the constraint—context length, speed, or cost—to the provider, and keep the abstraction layer thin so you can move.

Tagslong-contextproviderslatencybenchmark

Written by

n4n Team

The team building n4n — a single OpenAI-compatible API in front of 240+ models, with automatic fallback, load balancing and pay-per-token metering.

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