What happened
Artificial Analysis publishes a provider comparison for DeepSeek V4.1 Flash 1017 (Max), an open-weights model released in October 2026. The page says the model is available through 26 API providers and frames its analysis as support for choosing a provider for a particular use case. It compares cost, time per task, endpoint accuracy, latency, reliability, and API features.
For cost, Artificial Analysis calculates a weighted average cost per Intelligence Index task. Its overview lists DeepInfra first at $0.19 per task, followed by GMI at $0.30 and Wafer at $0.38. The page says cost varies up to 76.5 times across providers. The table shows that the provider prices incorporate several token categories, including input, cache hits, cache writes, reasoning, and answers, rather than representing a simple input-token rate. Its FAQ uses a different blended-token calculation, based on a 7:2:1 cache-hit, input, and output ratio, and names DeepInfra at $0.0511 per million tokens as the lowest-priced endpoint under that measure.
For time per task, Baseten (FAST) ranks first at 2.4 minutes, followed by Systalyze at 5.4 minutes and Inco (FAST) at 7.5 minutes. Artificial Analysis says time per task is based on output tokens per Intelligence Index task divided by end-to-end output speed, weighted by the index benchmarks. Its FAQ contrasts Baseten (FAST) with DeepInfra at 25.1 minutes, a 10.5-fold difference. The table also lists first-answer-token latency, request error rates, context windows, function calling, JSON mode, and license. All listed providers show a 1M context window and an open license; the page's FAQ says all 26 support JSON mode and function calling.
The Endpoint Accuracy Index compares each provider endpoint with a reference endpoint by rerunning BFCL v4-500, HLE-250, and AA-LCR-25. A score of 100% matches the reference; lower scores indicate lost accuracy, which the page attributes to quantisation, sampling defaults, or other endpoint configuration. Artificial Analysis describes these as point-in-time snapshots. It says speed and latency average runs over the past five days, error rates pool requests over that period, and benchmarking runs every six hours. The page recommends weighing task time, latency, pricing, and API features against the intended workload.
What the archive adds
New Runtime's earlier coverage on Artificial Analysis's Endpoint Accuracy Index v1.1 on 2026-11-06 described sharper long-context measurements and expanded open-weight coverage. This provider page applies that measurement approach to DeepSeek V4.1 Flash 1017 endpoints: a new model-specific comparison, not a restatement of the index launch.
Trend signal
This supports agent economics moves to completed work: the useful comparison here is cost and time per benchmark task, not just token prices. The task-weighted measure is still a benchmark proxy, not a verified cost per completed production workflow.
Why now
The model was released in October 2026 and is now listed across 26 API providers. As endpoints proliferate, the provider choice becomes a live operational decision rather than a theoretical comparison of model weights alone. Artificial Analysis also says endpoint performance can vary because of infrastructure changes, load balancing, and updates, making a current measurement more useful than a static pricing page.
Connections to other events
New Runtime's earlier coverage of Vals AI's Inference Provider Index on 2026-10-24 found up to 2.3-fold cost differences for the same model on agentic tasks, and that first-party APIs were neither consistently cheapest nor fastest. Artificial Analysis reaches the same operational question from a DeepSeek-specific benchmark: the serving endpoint, not only the underlying model, can determine the economics and pace of an agent workflow.
Why it matters
Teams can keep the same model while changing where inference runs, but this comparison suggests that doing so may materially change both bill and elapsed task time. A sensible evaluation should therefore include provider-level testing: run representative prompts and tool-use sessions, measure completion quality and failure rates, and calculate cost under the team's own token and caching mix.
The headline cheapest and fastest options optimize different objectives. DeepInfra's low task cost and Baseten (FAST)'s low task time are useful starting points for trials, not universal recommendations. A workload that needs rapid interactive turns may value first-answer latency and task duration differently from a batch workflow that primarily needs lower spend. Teams should also factor in reliability, structured-output and tool-call support, and whether endpoint accuracy remains acceptable for their workload. Such checks can prevent a nominally cheap endpoint from increasing review costs or slowing an agent loop.
What happens next
- More teams will treat endpoint choice as a recurring benchmark decision rather than a one-time model selection. (3 months, 70%). Watch for: provider-specific routing and periodic endpoint evaluations.
- Price and speed rankings for this model will change as providers alter infrastructure or configuration. (3 months, 75%). Watch for: movement in Artificial Analysis's historical endpoint charts.
Limits and uncertainty
Artificial Analysis reports benchmark measurements, not results from each team's production traffic. Endpoint accuracy is relative to a reference and the page calls it a point-in-time snapshot; performance can change with provider infrastructure, load, and configuration. The displayed task cost is weighted for the Intelligence Index and may not match a team's prompt lengths, cache hit rates, reasoning-token use, or answer sizes. The comparison does not establish that the cheapest provider is best for any particular application, nor independently validate provider-specific service guarantees. It also does not show how provider results would translate to a team's particular tools, retrieval layer, concurrency level, or quality threshold.
Skill to build now
Build a small endpoint bake-off around one representative workflow. Send the same inputs and tool-use sequence to two or three candidate providers, record task completion, time, errors, and token-category charges, then compare results against a quality threshold. Include both an interactive sequence and a longer multi-turn task if those reflect production use. Re-run it periodically: Artificial Analysis says provider performance changes over time and updates benchmark runs every six hours.
Reader takeaway
The DeepSeek model is not one fixed price-speed proposition once it is served by 26 providers. Treat provider selection as part of model evaluation, and validate cost, speed, reliability, and accuracy against your own workload rather than choosing from a single leaderboard rank.