Llama 3.1 70B Instruct is Meta’s widely-used, instruction-tuned model for high-quality dialogue and tool use. With a ~131K-token context window, it can read long prompts and multi-file inputs—great for agents, RAG, and IDE assistants. But how “good” it feels in practice depends just as much on the inference provider as on the model: infra, batching, precision, and routing all shape speed, latency, and cost.

ArtificialAnalysis.ai benchmarks providers for Llama 3.1 70B on Time to First Token (snappiness), output speed (t/s), variance (predictability), scaling at longer input lengths, pricing, and end-to-end (E2E) time vs price. In this article, we use those figures (and an OpenRouter cross-check) to explain why DeepInfra is the practical choice—especially when you care about instant starts, predictable tails, and sane unit economics.

Throughout the article, we consistently mention the providers Amazon (Latency Optimized & Standard), Deepinfra, Google Vertex, Hyperbolic, Simplismart, and Together.ai to keep the comparison unbiased.

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The Llama 3.1 70B Instruct Model

Llama 3.1 70B Instruct is Meta’s mid-tier instruction-tuned model in the Llama 3.1 family (8B / 70B / 405B). It’s trained for high-quality dialogue and tool-centric workflows and is released as a text-in/text-out model across multiple languages.

Llama 3.1 bumps the context window to ~128K tokens (providers often show 131,072), which lets you pack long prompts, multi-file snippets, or retrieval chunks into a single request—critical for RAG, IDE assistants, and repo analysis.

Practical sizing guidance.

• Pick 70B when you need high-quality answers, tool use, and long-context workflows at reasonable latency/cost—often the best default for enterprise apps.
• Use 8B for ultra-low-latency or edge-ish deployments (quality trade-offs). 
• Reserve 405B for offline scoring, teacher/student distillation, or premium experiences where you can afford the inference bill.

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Speed & Predictability

Time to First Token (TTFT)

TTFT measures how quickly the first character streams back after you send a request. Sub-half-second TTFT feels “instant” in chat and IDE flows; longer than ~0.7s is perceptibly slower. On the ArtificialAnalysis (AA) TTFT chart for Llama 3.1 70B, the low bars mark the most responsive providers; DeepInfra sits in the leading group, while some routes focus on raw decode speed rather than first-token time.

https://artificialanalysis.ai/models/llama-3-1-instruct-70b/providers#time-to-first-token

DeepInfra lands in the instant-feel tier. DeepInfra Turbo (FP8) posts 0.31 s TTFT—only 0.04 s behind Google Vertex (0.27 s) and 0.03 s behind Together Turbo (0.28 s), and essentially tied with AWS Latency-Optimized (0.31 s). Versus slower routes, Turbo is markedly snappier: ~51% faster than Hyperbolic (0.63 s), ~52% faster than AWS Standard (0.64 s), and ~72% faster than Simplismart (1.12 s).

The standard DeepInfra endpoint also stays comfortably sub-second at 0.48s—~24% faster than Hyperbolic and ~25% faster than AWS Standard—while giving you a higher-precision option. Net result: whether you choose Turbo (FP8) for cost/throughput or the standard route for precision, DeepInfra delivers near-top TTFT that keeps chats and IDE assistants feeling responsive without paying a premium for the very first token.

Time to First Token Variance

Medians are nice; tails decide Service Level Agreements. The TTFT variance plot of ArtificialAnalysis (median + percentile spread) shows how often first-token time spikes under load. DeepInfra’s band is tight in our snapshot—good news for predictability and autoscaling. Providers with wider whiskers can feel “bursty,” forcing larger queues or more concurrency to hide tail latency. 

https://artificialanalysis.ai/models/llama-3-1-instruct-70b/providers#time-to-first-token-variance

• DeepInfra Turbo (FP8): 0.3 s median with a short IQR/whiskers—one of the tightest spreads on the chart. Operationally, that means first tokens arrive quickly and predictably across runs and concurrency levels.
  
• DeepInfra (standard): 0.5 s median, still sub-second with a compact spread—useful if you prefer higher precision while keeping p95/p99 stable.
  
• Peers for context: Together.ai Turbo and AWS Latency-Optimized also show 0.3 s medians with compact spreads; AWS Standard and Hyperbolic sit at 0.6 s; Simplismart is far slower at 1.1 s.
  
• Outlier risk: Google Vertex shows a 0.3s median but an exceptionally long max whisker (spiking into tens of seconds). That tail isn’t common, but when it hits, it can stall chats and trigger retries.
  

Takeaway: If you care about both snappy starts and consistent p95/p99, DeepInfra Turbo belongs in the top tier for Llama 3.1 70B, delivering near-best median TTFT with one of the tightest variance profiles on the board.

Latency by Context Length

The following chart tracks TTFT as your prompt grows (100 → 1k → 10k → 100k input tokens). It’s a direct read on prefill cost: the larger the context, the longer the model must process before it can emit the first token. If you do RAG with big chunks or repo-scale analysis, this curve matters more than the small-prompt median.

https://artificialanalysis.ai/models/llama-3-1-instruct-70b/providers#latency-by-input-token-count-context-length

DeepInfra Turbo (FP8) stays flat through short and medium prompts—0.3 s (100), 0.2–0.3 s (1k)—and remains quick at ~1.1 s (10k). At 100k, it posts 12.5s, which is among the lowest in the cohort and substantially better than several big-name routes.

For comparison, Google Vertex is excellent at short lengths (0.2–0.3 s) but rises to 1.2 s (10k) and 22.7 s (100k). DeepInfra (standard precision) stays sub-second on small inputs (0.3–0.5 s) but hits 2.3 s (10k) and 27.8 s (100k)—useful if you want higher precision, but Turbo is the better pick for extreme contexts. Simplismart climbs to 2.3 s (10k) and 22.3 s (100k), while AWS Standard degrades sharply (42.0 s at 10k and 42.5 s at 100k). AWS Latency-Optimized remains competitive at low lengths and shows a 14.8s bar at 100k.

If your application routinely pushes ≥10k–100k tokens, DeepInfra Turbo delivers one of the best large-context TTFT profiles: near-instant for short prompts and ~12.5 s at 100k—faster than Vertex (22.7 s), DeepInfra standard (27.8 s), Simplismart (22.3 s), and AWS Standard (42–42.5 s). That translates to a more responsive “first token” even when you stuff the prompt with long documents or multi-file inputs.

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Pricing

Speed wins hearts, but price determines what you can keep in production. Providers charge per million tokens, with separate rates for input (what you send) and output (what the model returns). Your input:output mix sets the blended cost per call—and tiny differences (¢0.10–¢0.30/M) snowball at scale. Just as importantly, pricing and performance are intertwined: a faster stack shrinks wall-clock time and concurrency needs, while a cheaper-per-token option can end up costing more if it’s slower or spikier.

Input and Output Prices

As of this writing, we have the following per-million rates for Llama 3.1 70B Instruct:

• DeepInfra — Standard & Turbo (FP8): both model pages list $0.40 / 1M tokens (DeepInfra)
  
• Together.ai — Turbo: Together’s announcement states Turbo endpoints at $0.88 for 70B; AA reflects this as $0.88 in / $0.88 out. (Together.ai)
• Hyperbolic: $0.40 / 1M Tokens as of their product website (Hyperbolic)

Please use the given sources to confirm the rate at the time that you are reading this article. 

https://artificialanalysis.ai/models/llama-3-1-instruct-70b/providers#pricing-input-and-output-prices

On Artificial Analysis for Llama 3.1 70B, input and output rates are symmetric per provider, meaning that the prices for input and output tokens are equal. For example, with 3,000 input tokens and 1,000 output tokens, we can calculate the prices using this formula:

Cost = (4,000 ÷ 1,000,000) × price_per_M

This gives the following prices: 

• DeepInfra / Hyperbolic ($0.40/M): $0.0016
  
• AWS Standard ($0.72/M): $0.00288 (≈ 80% more than DeepInfra)
  
• Together Turbo ($0.88/M): $0.00352 (≈ 120% more)
  
• AWS Latency-Optimized / Simplismart ($0.90/M): $0.00360 (≈ 125% more)
  

Because the rates are symmetric, the cheapest provider stays cheapest for any input:output mix—your cost scales with total tokens. That’s why pairing this chart with E2E vs. Price matters: if DeepInfra hits your latency SLO while charging $0.40/M, it delivers one of the lowest cost-per-completion profiles across realistic workloads.

Comparing End-to-End Response Time vs. Price

The following chart folds two opposing goals into one view: how fast a 500-token answer completes (y-axis, lower is better) against how much you pay per 1M tokens (x-axis, lower is better). Read it like a unit-economics map: the lower-left green box is the sweet spot—fast and inexpensive. Points to the right cost more; points higher take longer.

https://artificialanalysis.ai/models/llama-3-1-instruct-70b/providers#end-to-end-response-time-vs-price

DeepInfra Turbo (FP8) resides within the attractive quadrant at $0.40/M, with an end-to-end time of around 10 seconds. That pairing—sub-dollar pricing and sub-15-second completions—makes it a strong value choice when you want responsive UX without pushing $/M up the curve. DeepInfra (standard precision) remains cost-efficient ($0.40/M) but completes closer to ~30 s, which is outside the target box; pick this when precision matters more than wall-clock.

For context, Hyperbolic also prices at $0.40/M and posts the fastest run in this snapshot (≈4–5 s), while Together Turbo (~$0.88/M) and AWS Latency-Optimized (~$0.90/M) are quick (≈3–6 s) but live to the right of the green box due to higher token prices. AWS Standard (~$0.72/M, ≈20 s) and Simplismart (~$0.90/M, ≈6 s) illustrate the trade-offs on both axes.

How to use this: choose the lowest-cost point that still meets your latency SLO. If you need sub-8s completions, you may pay a premium with a right-hand provider; otherwise, DeepInfra Turbo offers a compelling balance—budget-friendly $/M with acceptable E2E and earlier charts showing near-top TTFT and tight variance.

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Independent validation (OpenRouter)

OpenRouter’s live routing page offers a provider-agnostic pulse on how Llama-3.1-70B Instruct performs in the wild—tracking average latency (s) and average throughput (tokens/sec) by provider, plus uptime. It’s a different dataset than ArtificialAnalysis (continuous, production-mixed traffic), so it’s useful to sanity-check the trends seen in AA’s controlled runs. 

Provider	Latency (avg, s)	Throughput (avg, tok/s)	Notes
DeepInfra	0.59	17.95	Sub-second latency in most snapshots; solid mid-pack throughput.
DeepInfra (Turbo)	0.33	50,42	Lowest latency in the pack with higher throughput than the standard version.
Hyperbolic	0.64	105.6	High throughput, but at the cost of a significantly higher latency compared to the top of tier.
Together.ai	0.33	113.3	Highest throughput and lowest latency of all providers. But comes at nearly double the price.
Simplismart	–	–	No OpenRouter stats
Amazon	–	–	No OpenRouter stats

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Why choose DeepInfra for Llama 3.1 70B Instruct?

For coding copilots, IDE plug-ins, and RAG agents, you need three things: instant starts, predictable tails, and sane $/M as prompts get long. DeepInfra checks all three. Turbo (FP8) delivers sub-half-second TTFT with one of the tightest variance profiles, so p95/p99 stay steady under load. Its large-context curve is flat through short and medium prompts and remains competitive at 100k tokens, keeping first tokens flowing even when you stuff the window. 

On price, DeepInfra’s $0.40/M for Llama 3.1 70B (Turbo and Standard) undercuts the $0.72–$0.90+ cohort, which shows up as a lower cost-per-completion once you hit production volumes. In AA’s E2E-vs-Price view, DeepInfra Turbo sits in the attractive quadrant—low cost with acceptable wall-clock—while others tend to trade one dimension for the other (faster but pricier, or cheaper but slower). OpenRouter’s live stats reinforce the picture: sub-second average latency with solid throughput. Put together, DeepInfra is the most balanced choice for shipping Llama 3.1 70B Instruct to production—fast enough to feel instant, predictable enough for SLAs, and priced to scale.

Disclaimer: This article reflects data and pricing as of October 23, 2025. Benchmarks and provider rates can change quickly, so please verify the linked sources and rerun representative evaluations before making decisions.