Ling 3.1 Flash vs Step 3.7 Flash
InclusionAI (Ant Group) · China | StepFun · China · Updated June 2026
Quick verdict
Pick Ling 3.1 Flash for hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work or artificial analysis intelligence index score of 41 — above average for its price tier. Pick Step 3.7 Flash for a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows or scmp reported it 'outshines larger rivals' from deepseek and moonshot on some benchmarks despite its smaller active size. Choose Step 3.7 Flash if you need self-hosting or data privacy; Ling 3.1 Flash if you want a managed API.
Ling 3.1 Flash (InclusionAI (Ant Group)) and Step 3.7 Flash (StepFun) are two of the models people most often weigh against each other in 2026. Ling 3.1 Flash is inclusionAI's (Ant Group) September 30, 2026 hybrid-reasoning MoE model — 560B parameters aimed at agents and long documents, launched as a free trial with weights still unreleased. Step 3.7 Flash is stepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. They diverge most on price and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: Step 3.7 Flash is about 1.5× cheaper on input ($0.2/$1.15 per 1M tokens vs $0.3/$0.9 per 1M tokens) — modest, but it adds up at steady volume.
- ▸Context window: both advertise 262K tokens (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Recency: Ling 3.1 Flash is the newer model by about 4 months (released September 30, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Ling 3.1 Flash | Step 3.7 Flash |
|---|---|---|
| Provider | InclusionAI (Ant Group) (China) | StepFun (China) |
| Released | September 30, 2026 | May 29, 2026 |
| Context window | 262K tokens (~393 pages) | 256K (~393 pages) |
| Price (in/out) | $0.3/$0.9 per 1M tokens | $0.2/$1.15 per 1M tokens |
| Open weight? | No — API only | Yes — self-hostable |
| Modalities | text, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Hybrid-reasoning MoE — 560B total parameters, ~25B active per token, aimed at agents, tool use, and long-document work
Ling 3.1 Flash
Step 3.7 Flash is comparatively weak here — smaller ecosystem and less third-party documentation than the more established Chinese labs
Artificial Analysis Intelligence Index score of 41 — above average for its price tier
Ling 3.1 Flash
InclusionAI's (Ant Group) September 30, 2026 hybrid-reasoning MoE model — 560B parameters aimed at agents and long documents, launched as a free trial with weights still unreleased — and it is the newer of the two.
First-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok)
Ling 3.1 Flash
Ling 3.1 Flash lists first-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok) among its strengths; Step 3.7 Flash does not.
A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows
Step 3.7 Flash
Ling 3.1 Flash is comparatively weak here — shipped as an announcement plus a two-week free trial rather than a full release — no weights, no model card, and limited independent verification at launch
SCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size
Step 3.7 Flash
Ling 3.1 Flash is comparatively weak here — context window currently served at 262K tokens even though InclusionAI's stated target is 1M — the larger window isn't live yet
Open weights (Apache 2.0) at a low per-token price
Step 3.7 Flash
At $0.2/$1.15 per 1M tokens it undercuts Ling 3.1 Flash ($0.3/$0.9 per 1M tokens), and that gap compounds at volume.
Lowest cost at scale
Step 3.7 Flash
At $0.2/$1.15 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Step 3.7 Flash
At $0.2/$1.15 per 1M tokens it undercuts Ling 3.1 Flash, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs
→ Step 3.7 Flash
Open weights let you run it on your own hardware; Ling 3.1 Flash is API-only.
Anyone whose priority is hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work
→ Ling 3.1 Flash
It is specifically built for that.
Anyone whose priority is a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows
→ Step 3.7 Flash
That is its strongest area.
Ling 3.1 Flash: where it fits
InclusionAI's (Ant Group) September 30, 2026 hybrid-reasoning MoE model — 560B parameters aimed at agents and long documents, launched as a free trial with weights still unreleased. Released September 30, 2026 by InclusionAI (Ant Group), it is built for hybrid-reasoning MoE — 560B total parameters, ~25B active per token, aimed at agents, tool use, and long-document work, artificial Analysis Intelligence Index score of 41 — above average for its price tier, first-party pricing of $0.30/$0.90 per million tokens, with an 80% cache-read discount ($0.06/MTok), and predecessor Ling 3.0 Flash shipped MIT-licensed open weights, suggesting a similar path once 3.1 clears review.
Its trade-offs are real: shipped as an announcement plus a two-week free trial rather than a full release — no weights, no model card, and limited independent verification at launch, context window currently served at 262K tokens even though InclusionAI's stated target is 1M — the larger window isn't live yet, not yet open-weight despite InclusionAI saying it plans to open-source the model 'soon', and announced via WeChat rather than a formal English-language technical report, so some specs are vendor-reported only. At $0.3 in / $0.9 out per million tokens, it sits in the budget price band.
Step 3.7 Flash: where it fits
StepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights. Released May 29, 2026 by StepFun, it is built for a 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows, sCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size, open weights (Apache 2.0) at a low per-token price, and includes a 1.8B vision encoder for image understanding alongside text.
Its trade-offs: stepFun is a newer, less established lab than DeepSeek, Alibaba, or Moonshot, benchmark 'outshines rivals' claims are from press coverage of specific tests, not a full independent leaderboard sweep, and smaller ecosystem and less third-party documentation than the more established Chinese labs. At $0.2 in / $1.15 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. Step 3.7 Flash gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Ling 3.1 Flash 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 Ling 3.1 Flash and Step 3.7 Flash 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 pricingFrequently asked questions
Is Ling 3.1 Flash or Step 3.7 Flash 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, Ling 3.1 Flash leans toward hybrid-reasoning moe — 560b total parameters, ~25b active per token, aimed at agents, tool use, and long-document work while Step 3.7 Flash leans toward a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Ling 3.1 Flash or Step 3.7 Flash?
Step 3.7 Flash is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Ling 3.1 Flash is API-metered at $0.3/$0.9 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?
Both advertise 262K tokens (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Ling 3.1 Flash and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you Ling 3.1 Flash, Step 3.7 Flash 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, Ling 3.1 Flash or Step 3.7 Flash?
Ling 3.1 Flash — released September 30, 2026, about 4 months after Step 3.7 Flash.
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.