Kimi K2 0905 is Moonshot’s long-context Mixture-of-Experts update designed for agentic and coding workflows. With a context window up to ~256K tokens, it can ingest large codebases, multi-file documents, or long conversations and still deliver structured, high-quality outputs. But real-world performance isn’t defined by the model alone—it’s determined by the inference provider that serves it: infrastructure, precision, batching, and routing all shape speed, latency, and cost.

Independent benchmarks from ArtificialAnalysis.ai compare providers head-to-head on output speed, variance, time-to-first-token (TTFT), end-to-end response time, scaling at longer input lengths, and pricing. In this article, we apply those benchmarks to Kimi K2 0905 and show why DeepInfra is among the top providers for this model—combining fast, predictable throughput with competitive per-million token rates.

Here’s how the post is structured: 

1. Analyzing performance by examining the time-to-first-token and its variance
2. Compare pricing for the different providers, including input and output tokens
3. Put pricing and speed into perspective using a diagram for End-to-End Response Time vs. Price
4. Reinforce the findings by double-checking with OpenRouter observations on latency and throughput.

Throughout the article, we consistently mention the providers Fireworks, Groq, Parasail, Together.ai, and DeepInfra to keep the comparison unbiased.

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API Speed & Predictability

Time to First Token

To measure the speed of the providers, we start with Time to First Token (TTFT)—the delay between sending a request and seeing the first character stream back. TTFT is what makes an assistant feel “instant”: sub-half-second responses reduce perceived wait and keep developers in flow, especially inside IDEs and chat-driven tools. It captures frontend snappiness, network and scheduler overhead, and how quickly a provider can begin decoding after prefill.

DeepInfra delivers a 0.33s TTFT, putting it firmly in the “feels instant” zone and second overall—just ~100 ms behind Groq (0.23s) while clearly ahead of Together (0.39s), Parasail (0.46s), and Fireworks (0.86s). In practice, that means prompts start streaming almost immediately in IDEs and chat UIs, keeping users in flow during multi-step agent runs. These are the direct deltas: 

• vs Together: ~15% faster (0.33s vs 0.39s)
  
• vs Parasail: ~28% faster (0.33s vs 0.46s)
  
• vs Fireworks: ~62% faster (0.33s vs 0.86s)

Time to First Token Variance

Just as important is TTFT variance. A great median is useful, but what your users feel day-to-day is the tail: p95 and p99 outliers that stall interactions, trigger retries, and force you to overprovision concurrency. Tight TTFT distributions mean steadier SLAs, simpler autoscaling, and fewer UX hiccups under bursty load. In the charts below, read median TTFT for baseline responsiveness, then check p95/p99 to judge predictability—fast and consistent beats occasional sprints with long pauses.

DeepInfra lands at a 0.3s median with tight whiskers, indicating fast and repeatable first-token starts even under bursty load. It trails Groq only slightly (≈0.2s median) but is materially steadier than price-focused routes that show larger tails. Together.ai sits around 0.4s with a broader spread—fine on average, but with more p95/p99 wobble. Parasail is slower at ~0.5s yet fairly consistent. Fireworks is the outlier: a ~0.9s median and the widest tail (spiking to multi-second starts), which can stall chats and force over-provisioning.

Netting it out: for Kimi K2 0905, DeepInfra combines near-top TTFT with the kind of tight variance that keeps IDE assistants feeling instant and keeps autoscaling simple – exactly what you want for production SLAs.

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Kimi K2 API Pricing

Developers love speed, but pricing decides what you can afford to run every day. Providers bill per million tokens and split rates into input (what you send) and output (what the model writes). Your input:output ratio and blended price determine the real cost per completion—and small deltas (¢0.10–¢0.30/M) compound quickly at scale. Just as important, price interacts with performance: a faster stack can cut wall-clock and concurrency costs, while a cheaper-per-token route can still be more expensive in practice if it’s slower or spikier. 

Kimi K2 Input and Output Prices

As of this writing, we have the following per-million rates for K2 0905:

• DeepInfra: $0.40 cached in / $0.50 input / $2.00 output; 262,144 context; fp4 support. (deepinfra.com)
  
• Groq: $1.00 input / $3.00 output (Groq)
  
• Together.ai: $1.00 input / $3.00 output (together.ai)
  
• Fireworks: $0.60 input / $2.50 output (Fireworks AI)
  
• Parasail: $0.99 input / $2.99 output (app.langdb.ai)

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

If your workloads are input-heavy (RAG/repo reads) with short answers, DeepInfra’s $0.50 plus $0.40 cached input can materially undercut $1.00 tiers. For output-heavy completions, $2.00 out also beats the $2.29–$3.00 cluster. 

Example cost (one request): 3,000 input / 1,000 output tokens.

• DeepInfra: 3k×$0.50/1e6 + 1k×$2.00/1e6 ≈ $0.00150 + $0.00200 = $0.00350 (drops if much of the prompt is cache-eligible at $0.40/M).
  
• Groq/Together (1/3): ≈$0.00400.
  
• Fireworks (0.6/2.5): ≈$0.00310. 

Even when another provider is a few tenths of a mill on list price, end-to-end time and variance still drive user-perceived cost (waiting) and infra headroom. That’s why we also read the next chart.

Comparing End-to-End Response Time vs. Price

End-to-end response time vs. price collapses two competing goals into one picture: how fast a 500-token answer arrives (wall-clock seconds on the y-axis) and how much you pay per million tokens (x-axis). It’s the closest proxy to cost per completion at a target UX. A provider that’s cheap but slow will inflate wait times, concurrency, and operational overhead; a provider that’s blazing fast but pricey can blow up unit economics.

Reading this chart is simple: the lower-left (green) quadrant is the sweet spot—low cost and low E2E time. Points drifting right are more expensive; points drifting up are slower. Use it to pick the lowest-cost option that still meets your latency SLO: if a provider sits comfortably under your E2E threshold at a meaningfully lower $/M, it’s the economical choice. If you need sub-8s or tighter, you may trade some $/M for speed—but the diagram makes that trade explicit.

DeepInfra anchors the low-price side at roughly $0.88/M with an end-to-end time of about 11–12s. That positioning makes it the value choice in this set: you pay materially less per million tokens while still getting sub-second first-token responsiveness elsewhere in the benchmarks (~0.33s), so interactions start quickly and then stream at a pace that’s acceptable for many agent steps and UI updates.

For context, Fireworks sits around $1.21/M and ~6.7s E2E (solidly in the green zone), while Groq trades the lowest latency (~1.6s) for the highest price in the cohort ($1.50/M). Parasail (~$1.50/M, ~8.5s) just misses the green box on latency, and Together.ai (~$1.50/M, ~13s) is both pricier and slower than DeepInfra in this scenario.

If your target is the lowest cost per completion with responsive first-token behavior, DeepInfra’s point on this chart is compelling. If you have a strict E2E SLO under ~8s for 500-token completions, Fireworks (higher cost) or Groq (highest cost) may hit that bar; otherwise, DeepInfra delivers the most economical path to production for Kimi K2 0905 without sacrificing the “starts instantly” feel.

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Independent validation (OpenRouter)

OpenRouter’s live routing stats give a second, provider-agnostic look at latency (seconds to first token/response) and throughput (avg tokens/sec). These aren’t the same runs as ArtificialAnalysis, but they’re useful to sanity-check trends seen there.

Provider	Latency (avg, s)	Throughput (avg, tok/s)	Notes
DeepInfra	0.60	54.97	Lowest average latency in this set; solid mid-pack throughput.
Fireworks	0.83	74.23	Higher latency than DeepInfra; higher throughput.
Groq	0.68	389.87	Best throughput by a wide margin; latency slightly above DeepInfra.
Together.ai	0.81	50.04	Similar throughput to DeepInfra but slower latency.
Parasail	–	–	No OpenRouter stats

Key takeaways

• DeepInfra posts the best average latency (0.60s), beating Groq (0.68s), Together (0.81s), and Fireworks (0.83s). That lines up with the “instant feel” you want for assistants and multi-step agents.
  
• Groq leads raw throughput (≈390 tok/s) but with higher $/M; Fireworks is faster than DeepInfra on throughput yet slower on latency.
  
• Cost-effectiveness story holds: When you pair OpenRouter’s latency win for DeepInfra with its blended/input pricing, you get one of the lowest cost-per-responsive-completion profiles in the cohort.

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Why DeepInfra wins for K2 0905

For coding assistants, IDE plug-ins, and repo-scale analysis, you want fast tokens/sec, tight variance (predictable p95), long-context stability, and fair $/M. DeepInfra combines a high, steady decode rate up through 10k-token prompts with competitive per-million prices—plus cached-input billing that reduces costs on repeated or chunked context. In AA’s E2E vs price view, DeepInfra sits in or near the most attractive quadrant for this model in our snapshot, while several competitors trade one dimension for the other (faster but dearer, or cheaper but slower). Cross-checks on OpenRouter confirm long context and healthy routing/uptime surfacing for K2 0905. Put together, DeepInfra is the most balanced choice for shipping K2 0905 into production.

Disclaimer: This article reflects data and pricing as of October 3, 2025. Benchmarks and provider rates can change quickly, so please verify the linked sources and rerun representative evaluations before making decisions.