DeepSeek R1 vs Step 5 Preview

DeepSeek · China  |  StepFun · China · Updated June 2026

Quick verdict

Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. Pick Step 5 Preview for sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents or 1m-token context window (64k max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available. Choose DeepSeek R1 if you need self-hosting or data privacy; Step 5 Preview if you want a managed API.

DeepSeek R1 (DeepSeek) and Step 5 Preview (StepFun) are two of the models people most often weigh against each other in 2026. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. Step 5 Preview is stepFun's September 20, 2026 flagship preview — a 600B-parameter sparse MoE model (27B active) with a 1M-token context window at $1/$2.70 per million tokens, open weights promised for mid-October. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecDeepSeek R1Step 5 Preview
ProviderDeepSeek (China) StepFun (China)
ReleasedJanuary 2025 September 20, 2026
Context window128K (~192 pages) 1M tokens (~1,500 pages)
Price (in/out)$0.55/$2.19 per 1M tokens $1/$2.7 per 1M tokens
Open weight?Yes — self-hostable No — API only
Modalitiestext, code text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Open-weight reasoning model

DeepSeek R1

Open weights make this possible at all — Step 5 Preview is API-only, so it cannot leave the vendor's servers.

Transparent chain-of-thought

DeepSeek R1

The open-weight reasoning model that reset price expectations in early 2025 — and it runs cheaper at $0.55/$2.19 per 1M tokens.

Low cost

DeepSeek R1

At $0.55/$2.19 per 1M tokens it undercuts Step 5 Preview ($1/$2.7 per 1M tokens), and that gap compounds at volume.

Sparse MoE architecture — 600B total parameters, only 27B active per token — built for long-horizon agentic work across large codebases and documents

Step 5 Preview

Its 1M tokens window holds about 7.8× more than DeepSeek R1's 128K in a single prompt.

1M-token context window (64K max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available

Step 5 Preview

DeepSeek R1 is comparatively weak here — smaller 128K context

67.7% on DeepSWE v1.1, ahead of open-weight rivals Kimi K3 (67.5%) and GLM-5.3 (66.9%)

Step 5 Preview

StepFun's September 20, 2026 flagship preview — a 600B-parameter sparse MoE model (27B active) with a 1M-token context window at $1/$2.70 per million tokens, open weights promised for mid-October — and it carries the larger 1M tokens context.

Lowest cost at scale

DeepSeek R1

At $0.55/$2.19 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Step 5 Preview

Its 1M tokens window is about 7.8× larger than DeepSeek R1's 128K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ DeepSeek R1

At $0.55/$2.19 per 1M tokens it undercuts Step 5 Preview, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Step 5 Preview

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ DeepSeek R1

Open weights let you run it on your own hardware; Step 5 Preview is API-only.

Anyone whose priority is open-weight reasoning model

→ DeepSeek R1

It is specifically built for that.

Anyone whose priority is sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents

→ Step 5 Preview

That is its strongest area.

DeepSeek R1: where it fits

The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.

Its trade-offs are real: discontinued - no longer offered as a standalone model via DeepSeek's API as of mid-2026; the legacy alias that used to point to it stopped working July 24, 2026, older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget price band.

Step 5 Preview: where it fits

StepFun's September 20, 2026 flagship preview — a 600B-parameter sparse MoE model (27B active) with a 1M-token context window at $1/$2.70 per million tokens, open weights promised for mid-October. Released September 20, 2026 by StepFun, it is built for sparse MoE architecture — 600B total parameters, only 27B active per token — built for long-horizon agentic work across large codebases and documents, 1M-token context window (64K max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available, 67.7% on DeepSWE v1.1, ahead of open-weight rivals Kimi K3 (67.5%) and GLM-5.3 (66.9%), and scores 44 on the Artificial Analysis Intelligence Index, ranking 27th of 653 models tracked.

Its trade-offs: trails closed frontier models on coding — 67.7% on DeepSWE v1.1 vs GPT-6 Astra's 74.1% and Claude Opus 5's 74.0%, open weights were promised for October 15, 2026 but hadn't shipped as of this writing — API-only for now despite the open-weight roadmap, a 'Preview' release, not StepFun's finished flagship — positioning and pricing may change once weights ship, and stepCodeBench and DeepSWE figures are StepFun's own reported numbers, not yet independently reproduced at scale. At $1 in / $2.7 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

The defining split here is open vs. closed. DeepSeek R1 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Step 5 Preview gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both DeepSeek R1 and Step 5 Preview without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is DeepSeek R1 or Step 5 Preview better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek R1 leans toward open-weight reasoning model while Step 5 Preview leans toward sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, DeepSeek R1 or Step 5 Preview?

DeepSeek R1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Step 5 Preview is API-metered at $1/$2.7 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

Step 5 Preview — 1M tokens vs 128K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both DeepSeek R1 and Step 5 Preview together?

Yes — a multi-model platform like LumiChats gives you DeepSeek R1, Step 5 Preview and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, DeepSeek R1 or Step 5 Preview?

Step 5 Preview — released September 20, 2026, about 20 months after DeepSeek R1.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.