Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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. On a tight budget at scale, Step 3.7 Flash is the value pick.
DeepSeek R1 (DeepSeek) and Step 3.7 Flash (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 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 context window — each quantified below from the models' real specs.
Key differences
Price: Step 3.7 Flash is about 2.8× cheaper on input ($0.2/$1.15 per 1M tokens vs $0.55/$2.19 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Step 3.7 Flash holds 2× more — 256K (~393 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Step 3.7 Flash is the newer model by about 16 months (released May 29, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek R1
Step 3.7 Flash
Provider
DeepSeek (China)
StepFun (China)
Released
January 2025
May 29, 2026
Context window
128K (~192 pages)
256K (~393 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
Yes — self-hostable
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
Open-weight reasoning model: DeepSeek R1 — DeepSeek R1 lists open-weight reasoning model among its strengths; Step 3.7 Flash does not.
Transparent chain-of-thought: DeepSeek R1 — DeepSeek R1 lists transparent chain-of-thought among its strengths; Step 3.7 Flash does not.
Low cost: DeepSeek R1 — DeepSeek R1 lists low cost 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 — DeepSeek R1 is comparatively weak here — 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
SCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size: Step 3.7 Flash — StepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights — and it runs cheaper at $0.2/$1.15 per 1M tokens.
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 DeepSeek R1 ($0.55/$2.19 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.
Largest single-prompt input: Step 3.7 Flash — Its 256K window is about 2× larger than DeepSeek R1's 128K, fitting roughly 393 pages in one prompt.
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 DeepSeek R1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Step 3.7 Flash — Larger 256K window fits more in one prompt.
Anyone whose priority is open-weight reasoning model: DeepSeek R1 — 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.
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 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
DeepSeek R1 and Step 3.7 Flash overlap enough that the right pick depends on your specific job. Step 3.7 Flash costs less per token; Step 3.7 Flash holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, Step 3.7 Flash for a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is DeepSeek R1 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, DeepSeek R1 leans toward open-weight reasoning model 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, DeepSeek R1 or Step 3.7 Flash?
Step 3.7 Flash is cheaper — $0.55/$2.19 per 1M tokens vs $0.2/$1.15 per 1M tokens, roughly 2.8× apart on input.
Which has the bigger context window?
Step 3.7 Flash — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, 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, DeepSeek R1 or Step 3.7 Flash?
Step 3.7 Flash — released May 29, 2026, about 16 months after DeepSeek R1.
DeepSeek R1 vs Step 3.7 Flash
DeepSeek · China | StepFun · China · Updated June 2026
Quick verdict
Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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. On a tight budget at scale, Step 3.7 Flash is the value pick.
DeepSeek R1 (DeepSeek) and Step 3.7 Flash (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 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 context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Step 3.7 Flash is about 2.8× cheaper on input ($0.2/$1.15 per 1M tokens vs $0.55/$2.19 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Step 3.7 Flash holds 2× more — 256K (~393 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Step 3.7 Flash is the newer model by about 16 months (released May 29, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek R1
Step 3.7 Flash
Provider
DeepSeek (China)
StepFun (China)
Released
January 2025
May 29, 2026
Context window
128K (~192 pages)
256K (~393 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
Yes — self-hostable
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
Open-weight reasoning model
DeepSeek R1
DeepSeek R1 lists open-weight reasoning model among its strengths; Step 3.7 Flash does not.
Transparent chain-of-thought
DeepSeek R1
DeepSeek R1 lists transparent chain-of-thought among its strengths; Step 3.7 Flash does not.
Low cost
DeepSeek R1
DeepSeek R1 lists low cost 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
DeepSeek R1 is comparatively weak here — 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
SCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size
Step 3.7 Flash
StepFun's 198B MoE vision-language model built for coding and search agents - a distinct Chinese lab with genuinely competitive open weights — and it runs cheaper at $0.2/$1.15 per 1M tokens.
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 DeepSeek R1 ($0.55/$2.19 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.
Largest single-prompt input
Step 3.7 Flash
Its 256K window is about 2× larger than DeepSeek R1's 128K, fitting roughly 393 pages in one prompt.
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 DeepSeek R1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Step 3.7 Flash
Larger 256K window fits more in one prompt.
Anyone whose priority is open-weight reasoning model
→ DeepSeek R1
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.
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 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
DeepSeek R1 and Step 3.7 Flash overlap enough that the right pick depends on your specific job. Step 3.7 Flash costs less per token; Step 3.7 Flash holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, Step 3.7 Flash for a 198b-parameter sparse moe vision-language model (11b active) built specifically for agentic coding and search workflows. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both DeepSeek R1 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.
Is DeepSeek R1 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, DeepSeek R1 leans toward open-weight reasoning model 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, DeepSeek R1 or Step 3.7 Flash?
Step 3.7 Flash is cheaper — $0.55/$2.19 per 1M tokens vs $0.2/$1.15 per 1M tokens, roughly 2.8× apart on input.
Which has the bigger context window?
Step 3.7 Flash — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, 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, DeepSeek R1 or Step 3.7 Flash?
Step 3.7 Flash — released May 29, 2026, about 16 months after DeepSeek R1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.