Pick Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (text, image and video). 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 K3 (Moonshot AI) and Step 3.7 Flash (StepFun) are two of the models people most often weigh against each other in 2026. Kimi K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. 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 15× cheaper on input ($0.2/$1.15 per 1M tokens vs $3/$15 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Kimi K3 holds 4× more — 1M (~1,573 pages) vs 256K (~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: Kimi K3 is the newer model by about 59 days (released July 27, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Kimi K3
Step 3.7 Flash
Provider
Moonshot AI (China)
StepFun (China)
Released
July 27, 2026
May 29, 2026
Context window
1M (~1,573 pages)
256K (~393 pages)
Price (in/out)
$3/$15 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Largest open-weight model at release — 2.8T sparse MoE, self-hostable: Kimi K3 — Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it carries the larger 1M context.
1M-token context with native vision (text, image and video): Kimi K3 — Its 1M window holds about 4× more than Step 3.7 Flash's 256K in a single prompt.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness: Kimi K3 — Step 3.7 Flash is comparatively weak here — benchmark 'outshines rivals' claims are from press coverage of specific tests, not a full independent leaderboard sweep
A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows: Step 3.7 Flash — Kimi K3 is comparatively weak here — coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified
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 K3 ($3/$15 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: Kimi K3 — Its 1M window is about 4× larger than Step 3.7 Flash's 256K, fitting roughly 1,573 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 Kimi K3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Kimi K3 — Larger 1M window fits more in one prompt.
Anyone whose priority is largest open-weight model at release — 2.8t sparse moe, self-hostable: Kimi K3 — 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 K3: where it fits
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.
Its trade-offs are real: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 out per million tokens, it sits in the mid 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 K3 and Step 3.7 Flash overlap enough that the right pick depends on your specific job. Step 3.7 Flash costs less per token; Kimi K3 holds the larger context; and each leads in its own area — Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable, 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 K3 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 K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable 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 K3 or Step 3.7 Flash?
Step 3.7 Flash is cheaper — $3/$15 per 1M tokens vs $0.2/$1.15 per 1M tokens, roughly 15× apart on input.
Which has the bigger context window?
Kimi K3 — 1M vs 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Kimi K3 and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you Kimi K3, 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 K3 or Step 3.7 Flash?
Kimi K3 — released July 27, 2026, about 59 days after Step 3.7 Flash.
Kimi K3 vs Step 3.7 Flash
Moonshot AI · China | StepFun · China · Updated June 2026
Quick verdict
Pick Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (text, image and video). 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 K3 (Moonshot AI) and Step 3.7 Flash (StepFun) are two of the models people most often weigh against each other in 2026. Kimi K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. 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 15× cheaper on input ($0.2/$1.15 per 1M tokens vs $3/$15 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Kimi K3 holds 4× more — 1M (~1,573 pages) vs 256K (~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: Kimi K3 is the newer model by about 59 days (released July 27, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Kimi K3
Step 3.7 Flash
Provider
Moonshot AI (China)
StepFun (China)
Released
July 27, 2026
May 29, 2026
Context window
1M (~1,573 pages)
256K (~393 pages)
Price (in/out)
$3/$15 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Largest open-weight model at release — 2.8T sparse MoE, self-hostable
Kimi K3
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it carries the larger 1M context.
1M-token context with native vision (text, image and video)
Kimi K3
Its 1M window holds about 4× more than Step 3.7 Flash's 256K in a single prompt.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness
Kimi K3
Step 3.7 Flash is comparatively weak here — benchmark 'outshines rivals' claims are from press coverage of specific tests, not a full independent leaderboard sweep
A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows
Step 3.7 Flash
Kimi K3 is comparatively weak here — coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified
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 K3 ($3/$15 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
Kimi K3
Its 1M window is about 4× larger than Step 3.7 Flash's 256K, fitting roughly 1,573 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 Kimi K3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Kimi K3
Larger 1M window fits more in one prompt.
Anyone whose priority is largest open-weight model at release — 2.8t sparse moe, self-hostable
→ Kimi K3
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 K3: where it fits
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.
Its trade-offs are real: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 out per million tokens, it sits in the mid 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 K3 and Step 3.7 Flash overlap enough that the right pick depends on your specific job. Step 3.7 Flash costs less per token; Kimi K3 holds the larger context; and each leads in its own area — Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable, 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 K3 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 K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable 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 K3 or Step 3.7 Flash?
Step 3.7 Flash is cheaper — $3/$15 per 1M tokens vs $0.2/$1.15 per 1M tokens, roughly 15× apart on input.
Which has the bigger context window?
Kimi K3 — 1M vs 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Kimi K3 and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you Kimi K3, 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 K3 or Step 3.7 Flash?
Kimi K3 — released July 27, 2026, about 59 days after Step 3.7 Flash.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.