DeepSeek V4.1 Flash vs Ling 3.1 Flash
DeepSeek · China | InclusionAI (Ant Group) · China · Updated June 2026
Quick verdict
Pick DeepSeek V4.1 Flash for software engineering (deepswe v1.1: 74.2, essentially level with claude opus 5's 74.0) or 1m-token context window at a fraction of frontier pricing ($0.15/$0.60 per million tokens off-peak). 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. Choose DeepSeek V4.1 Flash if you need self-hosting or data privacy; Ling 3.1 Flash if you want a managed API.
DeepSeek V4.1 Flash (DeepSeek) and Ling 3.1 Flash (InclusionAI (Ant Group)) are two of the models people most often weigh against each other in 2026. DeepSeek V4.1 Flash is deepSeek's September 2026 flash-tier update adds native vision and a 1M-token window to its cost-efficient V4 line, at $0.15/$0.60 per million tokens. 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. 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
- ▸Price: DeepSeek V4.1 Flash is about 2× cheaper on input ($0.15/$0.6 per 1M tokens vs $0.3/$0.9 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: DeepSeek V4.1 Flash holds 4× more — 1.05M tokens (~1,573 pages) vs 262K tokens (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: Ling 3.1 Flash is the newer model by about 20 days (released September 30, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | DeepSeek V4.1 Flash | Ling 3.1 Flash |
|---|---|---|
| Provider | DeepSeek (China) | InclusionAI (Ant Group) (China) |
| Released | September 10, 2026 | September 30, 2026 |
| Context window | 1.05M tokens (~1,573 pages) | 262K tokens (~393 pages) |
| Price (in/out) | $0.15/$0.6 per 1M tokens | $0.3/$0.9 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, image | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Software engineering (DeepSWE v1.1: 74.2, essentially level with Claude Opus 5's 74.0)
DeepSeek V4.1 Flash
DeepSeek's September 2026 flash-tier update adds native vision and a 1M-token window to its cost-efficient V4 line, at $0.15/$0.60 per million tokens — and it runs cheaper at $0.15/$0.6 per 1M tokens.
1M-token context window at a fraction of frontier pricing ($0.15/$0.60 per million tokens off-peak)
DeepSeek V4.1 Flash
Its 1.05M tokens window holds about 4× more than Ling 3.1 Flash's 262K tokens in a single prompt.
Native multimodal vision, added over the text-only V4-Flash it replaces on most traffic
DeepSeek V4.1 Flash
DeepSeek's September 2026 flash-tier update adds native vision and a 1M-token window to its cost-efficient V4 line, at $0.15/$0.60 per million tokens — and it carries the larger 1.05M tokens context.
Hybrid-reasoning MoE — 560B total parameters, ~25B active per token, aimed at agents, tool use, and long-document work
Ling 3.1 Flash
DeepSeek V4.1 Flash is comparatively weak here — new Causal Encoder-Decoder architecture (8B active for input, 16B for output) is unproven at broader scale versus DeepSeek's mainline V4/V4-Pro line
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
DeepSeek V4.1 Flash is comparatively weak here — the $0.15/$0.60 pricing is an off-peak rate — cost reportedly doubles during peak-demand hours
Lowest cost at scale
DeepSeek V4.1 Flash
At $0.15/$0.6 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
DeepSeek V4.1 Flash
Its 1.05M tokens window is about 4× larger than Ling 3.1 Flash's 262K tokens, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek V4.1 Flash
At $0.15/$0.6 per 1M tokens it undercuts Ling 3.1 Flash, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ DeepSeek V4.1 Flash
Larger 1.05M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ DeepSeek V4.1 Flash
Open weights let you run it on your own hardware; Ling 3.1 Flash is API-only.
Anyone whose priority is software engineering (deepswe v1.1: 74.2, essentially level with claude opus 5's 74.0)
→ DeepSeek V4.1 Flash
It is specifically built for that.
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
That is its strongest area.
DeepSeek V4.1 Flash: where it fits
DeepSeek's September 2026 flash-tier update adds native vision and a 1M-token window to its cost-efficient V4 line, at $0.15/$0.60 per million tokens. Released September 10, 2026 by DeepSeek, it is built for software engineering (DeepSWE v1.1: 74.2, essentially level with Claude Opus 5's 74.0), 1M-token context window at a fraction of frontier pricing ($0.15/$0.60 per million tokens off-peak), native multimodal vision, added over the text-only V4-Flash it replaces on most traffic, and mIT-licensed open weights, self-hostable.
Its trade-offs are real: the $0.15/$0.60 pricing is an off-peak rate — cost reportedly doubles during peak-demand hours, new Causal Encoder-Decoder architecture (8B active for input, 16B for output) is unproven at broader scale versus DeepSeek's mainline V4/V4-Pro line, and positioned as a cost/speed tier, not a frontier-reasoning flagship — DeepSeek kept V4-Pro running in parallel rather than replacing it outright. At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
The defining split here is open vs. closed. DeepSeek V4.1 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 DeepSeek V4.1 Flash and Ling 3.1 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 DeepSeek V4.1 Flash or Ling 3.1 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, DeepSeek V4.1 Flash leans toward software engineering (deepswe v1.1: 74.2, essentially level with claude opus 5's 74.0) while 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, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4.1 Flash or Ling 3.1 Flash?
DeepSeek V4.1 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?
DeepSeek V4.1 Flash — 1.05M tokens vs 262K tokens, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4.1 Flash and Ling 3.1 Flash together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4.1 Flash, Ling 3.1 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, DeepSeek V4.1 Flash or Ling 3.1 Flash?
Ling 3.1 Flash — released September 30, 2026, about 20 days after DeepSeek V4.1 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.