ModelsArchitectures & capability
StepFun's Step 5 Preview coding model reaches OpenRouter, with a free week in Kilo Code
StepFun announced Step 5 Preview on September 20 and listed it on OpenRouter on October 8. The coding model uses a 600B-parameter mixture-of-experts design and costs $1 per million input tokens and $2.70 per million output tokens. Its benchmark results are mixed, and it is weakest on terminal-based agent tasks.
From the r/AISEOInsider (via last30days) article of October 13, 2026 and cited sources: Step 5 Preview is a sparse mixture-of-experts model with 600B total parameters, of which 27B are active per token. [3] [4] [5] [8]
SOURCES[3] CellCog: Step 5 Preview — Specs, Price, Benchmarks, and… cellcog.ai[4] RuntimeWire: StepFun launches a 600B agent model at $1 per… runtimewire.com[5] LLM Reference: Step 5 Preview on OpenRouter llmreference.com
AT Pulse research deskPublished · Updated · 5 min read · AI-assisted analysisAI-assisted, not human-edited. Methodology Corrections Who owns this site
- What happened
- Step 5 Preview is a sparse mixture-of-experts model with 600B total parameters, of which 27B are active per token. [3] [4] [5] [8]
- Original source
- r/AISEOInsider (via last30days) article, independent perspective
- Brief by
- AT Pulse research desk AI-assisted; publisher and contact on the desk page
- When
- October 13, 2026 article published
- Covered
- Oct 13 edition
- Evidence
- 12 sources across 11 sites
- 1How I’d Test the Step 5 Preview AI Model in One Afternoonreddit.com
- 2Linked video: How I’d Test the Step 5 Preview AI Model in…youtube.com
- 3CellCog: Step 5 Preview — Specs, Price, Benchmarks, and…cellcog.ai
- 4RuntimeWire: StepFun launches a 600B agent model at $1 per…runtimewire.com
- 5LLM Reference: Step 5 Preview on OpenRouterllmreference.com
- 6OrcaRouter: Step 5 Preview free Kilo Code weekorcarouter.ai
RisingHow it’s measured
YouTube video · 1.8K views · 14 likes
Reach blends public engagement (views, likes, comments, shares, stars) from the platforms where the story or its original source lives: YouTube, Hacker News, GitHub, Substack and similar. A video and the article written from its transcript share one score, taken from the video. Platforms with no data are left out rather than counted as zero, and the age of a story never lowers its score.
Step 5 Preview is a sparse mixture-of-experts model with 600B total parameters, of which 27B are active per token. It takes text, image and video input and returns text. It has a 1M-token context window, up to 64K output tokens, tool calling and structured JSON output. Input tokens cost $1 per million ($0.05 on a cache hit) and output tokens cost $2.70 per million, with reasoning tokens billed as output. StepFun announced the model on September 20. It reached OpenRouter on October 8, where StepFun is the only provider and serves it at fp8 precision. [3] [4] [5] [8]
On StepFun's own benchmarks, the model narrowly beats Kimi K3 and GLM 5.3 on DeepSWE v1.1 (67.7 vs 67.5 and 66.9), ProgramBench and StepFun's own StepCodeBench. In the same table it trails rivals on Terminal-Bench v4 (33.3, against 41.9 for GLM 5.3 and 57.9 for GPT-6 Astra) and on SWE-Marathon. Independent runs from Artificial Analysis (33.3) and vals.ai (31.82) give a similar Terminal-Bench 4.0 score. Some of StepFun's comparisons also used different effort settings for different models or covered different subsets of a dataset. [3] [7] [9]
Trying the model costs little: users can select it inside a coding agent they already use and switch back later. A fair comparison runs the same task and tests on two models several times, recording time taken, how often a person had to intervene and whether the tests passed. Prompts can also set rules against unrelated edits and unverified claims that tests passed. The free Kilo Code listing says prompts may be used for training, so proprietary code should stay off that tier. [2] [6]
low prices and easy access through existing coding agents make trials cheap, but measured Terminal-Bench scores trail frontier rivals.
Read the full assessment
Implication: teams can test it on real tasks now, but should not move long-running or terminal-heavy work to it until more evaluations back that up.
Status: I've collected the launch, access, benchmark and free-trial sources and am now writing the dossier.
Executive brief
StepFun's Step 5 Preview was not new on October 8. The model was announced September 20, 2026; October 8 is when it appeared on OpenRouter. Commentary built around the later date, such as Julian Goldie's video, gets the release date wrong. The model is a 600B-parameter mixture-of-experts system with 27B parameters active per token, a 1M-token context window and pricing of $1 / $2.70 per million input/output tokens. Its strongest coding numbers come from StepFun itself. The one tracked agentic-coding score with an independent source, Terminal-Bench 4.0 at about 33%, sits well behind frontier rivals.
What changed and event timeline
StepFun announces Step 5 Preview
The announcement went out on StepFun's site and X. The API went live as
step-5-preview, open weights were promised for October 15 and no license was named (;).OpenRouter listing
OpenRouter added
stepfun/step-5-previewwith StepFun as the only provider, serving fp8 (;).Free week in Kilo Code
A StepFun post made the model free in Kilo Code for one week. No end date is documented, and the free listing says prompts may be used for training ().
Independent scores accumulate
BenchLM's aggregation shows 13 Artificial Analysis results next to 23 self-reported ones ().
Creator test-plan video
The video calls Step 5 Preview "crazy good," gives October 8 as the launch date, and promotes a paid community (;).
Capabilities and access
- Model:
step-5-preview, a sparse MoE with 600B total and 27B active parameters (CellCog). - Inputs and outputs: text, up to 60 images, and video in; text out. Up to 1M tokens in and 64K out.
- Features: three reasoning-effort levels, tool calling, JSON Mode/Schema, prompt caching and streaming (LLM Reference).
Read the full section
- Model:
step-5-preview, a sparse MoE with 600B total and 27B active parameters (CellCog). - Inputs and outputs: text, up to 60 images, and video in; text out. Up to 1M tokens in and 64K out.
- Features: three reasoning-effort levels, tool calling, JSON Mode/Schema, prompt caching and streaming (LLM Reference).
- Pricing: $1.00 per million input tokens ($0.05 on a cache hit) and $2.70 per million output tokens. Reasoning tokens bill as output.
- Access: StepFun's API, OpenRouter and a free Kilo listing (OrcaRouter).
Technical analysis for researchers and developers
- No technical report. StepFun's public description of the routing is a single paragraph (CellCog).
- Step 5 Preview was tested at "High" effort while rivals ran at "Max."
- StepCodeBench and the FinStepBench suites were made by StepFun.
Read the full section
- No technical report. StepFun's public description of the routing is a single paragraph (CellCog).
- Mismatched settings. Step 5 Preview was tested at "High" effort while rivals ran at "Max." On HLE with tools, StepFun compared its text-only subset against other models' full-dataset scores and says the results are not directly comparable.
- Self-built benchmarks. StepCodeBench and the FinStepBench suites were made by StepFun.
- Long-run demos are vendor-run: a 24-hour H100 kernel optimization reaching 508 TFLOPS (Claude Opus 5: 493), and Pokémon Red played over more than 3,000 turns.
- OpenRouter serves fp8, so results there may differ from StepFun's own endpoint.
Claims and evidence
Read the full section
- Wins over Kimi K3 and GLM 5.3 on DeepSWE v1.1 (67.7 vs 67.5 and 66.9), ProgramBench (80.5) and StepCodeBench (49.0) are all self-reported (CellCog; BenchLM).
- It is behind on several benchmarks, in StepFun's own table:
- Terminal-Bench v4: 33.3, vs GLM 5.3 at 41.9 and GPT-6 Astra at 57.9.
- SWE-Marathon: 72.7, vs Kimi K3 at 84.4 and Claude Opus 5 at 85.6.
- On DeepSWE, Astra and Opus 5 score about 74.
- Independent results: Artificial Analysis gives an Intelligence Index of 43.7 and Terminal-Bench 4.0 of 33.3 (BenchLM). vals.ai puts Terminal-Bench 4.0 at 31.82 (BenchList).
- Cost: the claim that a task costs about one-eighth of Claude Opus 5 comes from StepFun (MarkTechPost).
Context and prior work
StepFun is a Shanghai lab led by CEO Jiang Daxin. It raised more than $718M in a B+ round in January 2026, and its strategy pairs open-weight models with a proprietary flagship (RuntimeWire). The rivals StepFun chose for its comparisons are the Chinese open-weight models Kimi K3 and GLM 5.3.
Read the full section
StepFun is a Shanghai lab led by CEO Jiang Daxin. It raised more than $718M in a B+ round in January 2026, and its strategy pairs open-weight models with a proprietary flagship (RuntimeWire). Before this model, its OpenRouter catalog included Step 3.5 Flash. The rivals StepFun chose for its comparisons are the Chinese open-weight models Kimi K3 and GLM 5.3. Against closed frontier models it compares mainly on price.
Limitations, safety and contested findings
- Release date: the video's October 8 conflicts with the September 20 announcement (video, 00:23).
- Weights: CellCog found no Step 5 repository on StepFun's Hugging Face account as of October 8.
- Weak spots: multimodal document work, with GDP.pdf at 14.8%, and a 43.0% hallucination score on AA-Omniscience (BenchLM).
Read the full section
- Release date: the video's October 8 conflicts with the September 20 announcement (video, 00:23).
- Weights: CellCog found no Step 5 repository on StepFun's Hugging Face account as of October 8. A Hugging Face repo under the name "SHSLab" claims to hold official weights under a "StepFun Community License," but no StepFun source confirms it.
- Weak spots: multimodal document work, with GDP.pdf at 14.8%, and a 43.0% hallucination score on AA-Omniscience (BenchLM).
- Free tier: prompts may be used for training.
Business and practitioner implications
- Testing is cheap. You can pick the model inside a tool you already use and switch back later (video, 05:07).
- Use a fair A/B test.
- Set guardrails in the prompt: no unrelated edits, no claiming tests passed without running them, and ask before risky actions (05:21).
Read the full section
- Testing is cheap. You can pick the model inside a tool you already use and switch back later (video, 05:07).
- Use a fair A/B test. Run the same task and tests on two models, several times, and record time taken, how often you had to step in, and whether the tests passed (05:38).
- Set guardrails in the prompt: no unrelated edits, no claiming tests passed without running them, and ask before risky actions (05:21).
- Keep proprietary code off the free Kilo tier, because of its training-use terms.
- Wait for independent coding evaluations before routing terminal-heavy or long-horizon work away from frontier models.
The source trail.
Sources (12)
How I’d Test the Step 5 Preview AI Model in One Afternoon
Feed excerpt only. A transcript of the video linked from this page is supplied (linked video auto captions; https://www.youtube.com/watch?v=bqS3oZQgAhg); automatic text may contain errors.
reddit.comLinked video: How I’d Test the Step 5 Preview AI Model in One Afternoon
linked video auto captions
www.youtube.com