[Step 5 Preview vs GLM-5.3 vs DeepSeek V4.1 Flash](/llm-comparison/): StepFun Enters the Frontier AI Race

StepFun has just entered a much bigger AI-model conversation. Step 5 Preview is the company’s new flagship model for agentic work, software engineering, professional knowledge work, and finance, and it is designed to compete directly with models such as GLM-5.3 and DeepSeek V4.1 Flash.

The headline specifications are unusual: 600B total parameters, only 27B active per token, a 1M-token context window, and vision input. StepFun’s published benchmark table places Step 5 Preview close to GLM-5.3 and Kimi K3 on several difficult tests, while DeepSeek V4.1 Flash brings a different efficiency approach with 552B total parameters and asymmetric 8B/16B active computation.

This makes Step 5 Preview vs GLM-5.3 vs DeepSeek V4.1 Flash a much more interesting comparison than simply asking which model has the biggest parameter count.

What Is Step 5 Preview?

Step 5 Preview is StepFun’s new flagship sparse Mixture-of-Experts model. StepFun reports 600B total parameters and 27B active parameters per token, with a 1M-token context window and vision input.

The model is explicitly built around agentic workloads. StepFun says it targets software engineering, long-running execution, professional knowledge work, finance, and multimodal tasks. It is available through StepFun’s products and API, with open weights planned for October 15, 2026.

That positioning is important. StepFun is not presenting Step 5 Preview as simply another chatbot. Its demonstrations include repository-level software work, long-horizon GPU kernel optimization, automated post-training experiments, large-scale research, spreadsheet creation, and financial analysis.

Step 5 Preview Parameters and Architecture

The Step 5 Preview 600B headline can be misleading if read like a dense 600B model.

It is a sparse MoE system:

The active parameter count is particularly important for comparing it with other MoE models. A 600B total parameter count does not mean every generated token runs through all 600B parameters.

For example, DeepSeek V4.1 Flash is reported at 552B total parameters, but its architecture uses 8B active parameters for input and 16B for output. DeepSeek describes this asymmetric design as a way to reduce inference cost while preserving a high capability ceiling.

So the more useful architectural comparison is:

Parameter counts alone do not establish intelligence or real-world speed.

Step 5 Preview Benchmarks

StepFun’s official benchmark page gives us a useful first look because it places Step 5 Preview beside GLM-5.3, Kimi K3, and other frontier models.

Here are several of the most relevant results:

These are StepFun-published comparisons, not a single independent benchmark run performed under one neutral harness. StepFun states that DeepSWE v1.1 uses the SWE-agent harness with temperature 1.0 and top-p 0.95. It also notes that some results, including HLE with tools, use different evaluation subsets and should not be treated as directly comparable.

That caveat matters when interpreting the table.

Where Step 5 Preview looks strong

The model’s clearest published advantages against GLM-5.3 are:

• StepCodeBench: 49.0% vs 40.2%
• ProgramBench: 80.5% vs 72.0%
• DeepSWE v1.1: 67.7% vs 66.9%
• FrontierFinance: 66.4 vs 64.1
• DRACO: 83.3% vs 82.3%

But GLM-5.3 remains ahead on Terminal-Bench v4, with 41.9% versus 33.3%.

That is exactly why a good Step 5 Preview review should not reduce the comparison to one benchmark.

Step 5 Preview vs GLM-5.3

The Step 5 Preview vs GLM-5.3 comparison is probably the most important part of this launch.

On StepFun’s published results, Step 5 Preview is ahead on several coding and finance tests, but GLM-5.3 remains competitive or ahead on others.

The table shows something more useful than a simple ranking: the models have different strengths.

StepFun itself describes Step 5 Preview as particularly strong in finance and long-running agentic work. GLM-5.3’s Terminal-Bench v4 result, meanwhile, is a meaningful counterpoint for developers interested specifically in terminal-based coding agents.

Step 5 Preview vs DeepSeek V4.1 Flash

The Step 5 Preview vs DeepSeek V4.1 Flash comparison is equally interesting because DeepSeek released V4.1 Flash only recently and uses a very different efficiency strategy.

DeepSeek V4.1 Flash is a 552B MoE model with a new Causal Encoder-Decoder architecture. DeepSeek reports 8B active parameters for input and 16B for output, native visual understanding, a smaller KV cache, and lower API costs.

DeepSeek’s published benchmark results include:

These figures come from DeepSeek’s own release documentation. They are useful, but they were not produced under exactly the same benchmark presentation as StepFun’s Step 5 Preview table.

Finance is where StepFun has a particularly interesting comparison

StepFun publishes the following results:

Step 5 Preview therefore has a strong showing in StepFun’s finance evaluation, but DeepSeek V4.1 Flash leads on LiveSearch. The result is not a clean sweep in either direction.

Step 5 Preview Coding Performance

The Step 5 Preview coding story is more interesting than a single SWE score.

StepFun created StepCodeBench to cover practical software engineering across:

• 553 repositories
• 9 task categories
• 20 application domains
• 33 programming languages

The categories include feature modification, bug repair, refactoring, documentation, performance tuning, code generation, CI/CD operations, code transformation, and environment setup.

Step 5 Preview scores 49.0% avg@4 on StepCodeBench.

StepFun also reports a 24-hour GPU-kernel optimization experiment in which Step 5 Preview reached 508 TFLOPS, compared with 493 TFLOPS for Claude Opus 5. In a separate automated post-training experiment, Step 5 Preview improved a Qwen3-30B-A3B model from 53.3% to 60% on AIME24, matching the reported Claude Opus 5 result while using fewer annotator tokens.

These are interesting long-horizon demonstrations because they test whether the model can keep working, evaluate feedback, and revise its approach rather than simply answer one coding prompt.

Step 5 Preview 1M Context

The Step 5 Preview 1M context window is another major part of the story.

A million-token context is useful for:

• large repositories
• long technical documentation
• research projects
• large financial datasets
• multi-file debugging
• long agent trajectories

But context size should not be confused with guaranteed reasoning quality across the entire context.

A model can technically accept one million tokens and still struggle to retrieve the right detail from a huge context. In production, retrieval, summarization, caching, and tool design can matter as much as the maximum context number.

The important point is that Step 5 Preview is being designed for workflows where the model needs to retain a large amount of state over long tasks.

Step 5 Preview Price

StepFun’s own announcement emphasizes the model’s position on the intelligence-cost Pareto frontier.

Current third-party Artificial Analysis reporting puts Step 5 Preview around $1 per million input tokens and $2.70 per million output tokens, with cached input around $0.05/M in its measured API configuration. Artificial Analysis also reported roughly 100 tokens/s output and around 2.96 seconds time to first token in its evaluation.

The exact price developers pay can vary by provider, promotions, subscription plans, caching, and routing.

That makes Step 5 Preview price interesting because the model is not trying to win purely by being the cheapest model available. The pitch is closer to getting frontier-level capability without frontier-level task costs.

Step 5 Preview vs DeepSeek V4.1 Flash: Cost and Architecture

DeepSeek’s architecture gives it an especially interesting cost advantage.

DeepSeek V4.1 Flash uses:

• 552B total parameters
• 8B active input parameters
• 16B active output parameters
• native multimodal understanding
• compressed KV cache
• peak/off-peak API pricing

DeepSeek says its V4.1 Flash KV cache requires only one-quarter the HBM and one-eighth the SSD storage compared with the previous generation. It also says off-peak rates are 50% of peak rates.

So the architectural competition is not simply:

600B vs 552B.

It is:

27B active MoE vs asymmetric 8B/16B active computation.

That is a much more meaningful engineering comparison.

StepFun’s Long-Horizon Agent Strategy

The strongest part of the StepFun AI model story may actually be its focus on long-running execution.

StepFun says Step 5 Preview sustained a Pokémon Red run for more than 3,000 turns and 6 million interaction tokens. In another experiment, it was given 24 hours to optimize an MLA GPU kernel.

Those demonstrations are designed around a specific problem with current AI agents: they often perform well on individual tasks but degrade when a task requires hundreds or thousands of dependent decisions.

StepFun is trying to make persistence itself part of the model’s capability.

That is particularly relevant for future coding agents.

Step 5 Preview and Open Weights

One major difference between Step 5 Preview and some previous StepFun releases is the timing of the open-weight release.

StepFun says the model will be released with open weights on October 15, 2026. The preview is currently available through StepFun’s products and API.

That creates an interesting split:

Today: API and product access.

October 15: planned open-weight availability.

For developers, the second date may ultimately be more important than the initial API launch because local deployment and independent inference testing could provide a much clearer picture of the model’s real-world efficiency.

A Reddit discussion about the model’s early Hugging Face appearance also showed community interest in its architecture, including a reported 352-expert configuration and 27B-scale active computation. The weights appeared briefly and were then removed, with users pointing back to the October 15 open-weight date. These community observations should be treated as preliminary until StepFun publishes the complete model card and release materials.

What Developers Are Saying

Early Reddit discussion is broadly focused on three things: benchmark quality, cost efficiency, and the upcoming open-weight release.

One r/singularity thread highlighted the model’s reported Artificial Analysis score and cost position, while commenters debated whether StepFun had genuinely entered the frontier tier or was simply benefiting from the timing of the current model cycle.

Another Reddit thread focused on the model’s architecture and the briefly visible Hugging Face weights. That discussion is useful for understanding developer interest, but it is not independent evidence of benchmark performance.

The safest conclusion from the early community reaction is that Step 5 Preview has attracted serious developer attention, but independent long-run testing is still needed.

Step 5 Preview vs GLM-5.3 vs DeepSeek V4.1 Flash

Here is the practical comparison:

*DeepSeek figures are from DeepSeek’s own release evaluation and should not be treated as a perfectly controlled cross-vendor test.

What Step 5 Preview Actually Changes

Step 5 Preview does not make GLM-5.3 or DeepSeek V4.1 Flash irrelevant.

Instead, it makes the frontier more crowded.

The interesting shift is that StepFun is now competing on several dimensions simultaneously:

• model intelligence
• agent reliability
• coding
• long-horizon execution
• professional knowledge work
• finance
• context length
• inference economics
• eventual open-weight availability

That is much harder than simply trying to win a benchmark.

The Step 5 Preview benchmarks already show that the model can sit close to GLM-5.3 and Kimi K3 on several tests. Its weaker Terminal-Bench v4 result also shows that there is still no reason to treat every benchmark as evidence of universal superiority.

The Bigger Story Behind StepFun

The launch also says something about the direction of Chinese AI models.

DeepSeek is pushing architectural efficiency with V4.1 Flash.

Z.ai is pushing GLM-5.3 and fast variants such as GLM-5.3-FlashX.

StepFun is now pushing a 600B sparse model with a relatively small active parameter footprint and an emphasis on long-running agent execution.

That means the competition is moving beyond:

“Who has the biggest model?”

toward:

“Who can complete useful work with the least cost, latency, and human intervention?”

For coding agents, that is the more important benchmark.

Final Take

Step 5 Preview is worth watching because it is not merely another large Chinese language model. StepFun is targeting the part of AI that matters most to developers right now: agents that can keep working for hours, operate across tools, modify real code, research large amounts of information, and produce finished work.

Its published results are strong against GLM-5.3 on several coding and finance benchmarks, while GLM-5.3 remains ahead on Terminal-Bench v4. DeepSeek V4.1 Flash has even stronger published numbers on several coding-agent tests, while Step 5 Preview shows advantages on several finance evaluations.

The next important milestone is October 15, 2026, when StepFun says open weights will arrive.

That is when the community can start testing the model outside StepFun’s own evaluation environment and answering the questions that launch benchmarks cannot fully settle:

How efficient is Step 5 Preview in real inference? How well does it perform in OpenCode and other coding agents? How does it compare with GLM-5.3 and DeepSeek V4.1 Flash on identical harnesses? And how much of its 600B parameter headline actually matters to developers?

Those tests will tell us considerably more than any single leaderboard number.

Sources

• StepFun - Step 5 Preview official announcement and benchmarks
• StepFun Open Platform
• DeepSeek - V4.1 Flash announcement
• DeepSeek API changelog and benchmark results
• Artificial Analysis reporting on Step 5 Preview
• Reddit discussion on Step 5 Preview
• Reddit discussion on the early Step 5 Preview weights