Pick Kimi K2.5 for native multimodal reasoning and visual coding or agentic tool-calling and self-directed multi-step work. 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.
Kimi K2.5 (Moonshot AI) and Step 3.7 Flash (StepFun) are two of the models people most often weigh against each other in 2026. Kimi K2.5 is moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. 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. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Price: Step 3.7 Flash is about 2.3× cheaper on input ($0.2/$1.15 per 1M tokens vs $0.45/$2.25 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Step 3.7 Flash is the newer model by about 4 months (released May 29, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Kimi K2.5
Step 3.7 Flash
Provider
Moonshot AI (China)
StepFun (China)
Released
January 27, 2026
May 29, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.45/$2.25 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Native multimodal reasoning and visual coding: Kimi K2.5 — Kimi K2.5 lists native multimodal reasoning and visual coding among its strengths; Step 3.7 Flash does not.
Agentic tool-calling and self-directed multi-step work: Kimi K2.5 — Kimi K2.5 lists agentic tool-calling and self-directed multi-step work among its strengths; Step 3.7 Flash does not.
Open-weight (Modified-MIT) — self-hostable at 256K context: Kimi K2.5 — Kimi K2.5 lists open-weight (Modified-MIT) — self-hostable at 256K context 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 — Kimi K2.5 is comparatively weak here — its coding score uses Moonshot's own harness, not the standard SWE-Bench Verified protocol
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 Kimi K2.5 ($0.45/$2.25 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 Kimi K2.5, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is native multimodal reasoning and visual coding: Kimi K2.5 — 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.
Kimi K2.5: where it fits
Moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. Released January 27, 2026 by Moonshot AI, it is built for native multimodal reasoning and visual coding, agentic tool-calling and self-directed multi-step work, open-weight (Modified-MIT) — self-hostable at 256K context, and vendor reports around 76.8% on its own SWE-agent coding harness.
Its trade-offs are real: its coding score uses Moonshot's own harness, not the standard SWE-Bench Verified protocol, superseded within Moonshot's line by Kimi K2.6 and K2.7, openRouter shows a promo price below Moonshot's $0.60/$2.50 list, and image input but no audio or video. At $0.45 in / $2.25 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
Kimi K2.5 and Step 3.7 Flash overlap enough that the right pick depends on your specific job. Step 3.7 Flash costs less per token; and each leads in its own area — Kimi K2.5 for native multimodal reasoning and visual coding, 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 Kimi K2.5 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, Kimi K2.5 leans toward native multimodal reasoning and visual coding 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, Kimi K2.5 or Step 3.7 Flash?
Step 3.7 Flash is cheaper — $0.45/$2.25 per 1M tokens vs $0.2/$1.15 per 1M tokens, roughly 2.3× apart on input.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Kimi K2.5 and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you Kimi K2.5, 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, Kimi K2.5 or Step 3.7 Flash?
Step 3.7 Flash — released May 29, 2026, about 4 months after Kimi K2.5.
Kimi K2.5 vs Step 3.7 Flash
Moonshot AI · China | StepFun · China · Updated June 2026
Quick verdict
Pick Kimi K2.5 for native multimodal reasoning and visual coding or agentic tool-calling and self-directed multi-step work. 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.
Kimi K2.5 (Moonshot AI) and Step 3.7 Flash (StepFun) are two of the models people most often weigh against each other in 2026. Kimi K2.5 is moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. 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. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Price: Step 3.7 Flash is about 2.3× cheaper on input ($0.2/$1.15 per 1M tokens vs $0.45/$2.25 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Step 3.7 Flash is the newer model by about 4 months (released May 29, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Kimi K2.5
Step 3.7 Flash
Provider
Moonshot AI (China)
StepFun (China)
Released
January 27, 2026
May 29, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.45/$2.25 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Native multimodal reasoning and visual coding
Kimi K2.5
Kimi K2.5 lists native multimodal reasoning and visual coding among its strengths; Step 3.7 Flash does not.
Agentic tool-calling and self-directed multi-step work
Kimi K2.5
Kimi K2.5 lists agentic tool-calling and self-directed multi-step work among its strengths; Step 3.7 Flash does not.
Open-weight (Modified-MIT) — self-hostable at 256K context
Kimi K2.5
Kimi K2.5 lists open-weight (Modified-MIT) — self-hostable at 256K context 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
Kimi K2.5 is comparatively weak here — its coding score uses Moonshot's own harness, not the standard SWE-Bench Verified protocol
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 Kimi K2.5 ($0.45/$2.25 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 Kimi K2.5, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is native multimodal reasoning and visual coding
→ Kimi K2.5
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.
Kimi K2.5: where it fits
Moonshot's open-weight multimodal model at $0.60/$2.50 with a 256K window — strong visual coding and agentic work, since superseded by K2.6/K2.7. Released January 27, 2026 by Moonshot AI, it is built for native multimodal reasoning and visual coding, agentic tool-calling and self-directed multi-step work, open-weight (Modified-MIT) — self-hostable at 256K context, and vendor reports around 76.8% on its own SWE-agent coding harness.
Its trade-offs are real: its coding score uses Moonshot's own harness, not the standard SWE-Bench Verified protocol, superseded within Moonshot's line by Kimi K2.6 and K2.7, openRouter shows a promo price below Moonshot's $0.60/$2.50 list, and image input but no audio or video. At $0.45 in / $2.25 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
Kimi K2.5 and Step 3.7 Flash overlap enough that the right pick depends on your specific job. Step 3.7 Flash costs less per token; and each leads in its own area — Kimi K2.5 for native multimodal reasoning and visual coding, 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 Kimi K2.5 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.
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, Kimi K2.5 leans toward native multimodal reasoning and visual coding 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, Kimi K2.5 or Step 3.7 Flash?
Step 3.7 Flash is cheaper — $0.45/$2.25 per 1M tokens vs $0.2/$1.15 per 1M tokens, roughly 2.3× apart on input.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Kimi K2.5 and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you Kimi K2.5, 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, Kimi K2.5 or Step 3.7 Flash?
Step 3.7 Flash — released May 29, 2026, about 4 months after Kimi K2.5.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.