Pick GPT-4o mini for very low cost per token for its capability tier or strong coding for a small model (87.2% humaneval). 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; GPT-4o mini if you want a managed API.
GPT-4o mini (OpenAI, US) and Step 3.7 Flash (StepFun, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GPT-4o mini is openAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. 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, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: GPT-4o mini is about 1.3× cheaper on input ($0.15/$0.6 per 1M tokens vs $0.2/$1.15 per 1M tokens) — modest, but it adds up at steady volume.
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 23 months (released May 29, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
GPT-4o mini
Step 3.7 Flash
Provider
OpenAI (US)
StepFun (China)
Released
July 18, 2024
May 29, 2026
Context window
128K (~192 pages)
256K (~393 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Very low cost per token for its capability tier: GPT-4o mini — At $0.15/$0.6 per 1M tokens it undercuts Step 3.7 Flash ($0.2/$1.15 per 1M tokens), and that gap compounds at volume.
Strong coding for a small model (87.2% HumanEval): GPT-4o mini — Step 3.7 Flash is comparatively weak here — smaller ecosystem and less third-party documentation than the more established Chinese labs
Leading MMLU among peer small models (82%): GPT-4o mini — OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch — and it runs cheaper at $0.15/$0.6 per 1M tokens.
A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows: Step 3.7 Flash — GPT-4o mini is comparatively weak here — weaker on hard reasoning and coding than frontier models
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 carries the larger 256K context.
Open weights (Apache 2.0) at a low per-token price: Step 3.7 Flash — Its 256K window holds about 2× more than GPT-4o mini's 128K in a single prompt.
Lowest cost at scale: GPT-4o mini — 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: Step 3.7 Flash — Its 256K window is about 2× larger than GPT-4o mini's 128K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GPT-4o mini — At $0.15/$0.6 per 1M tokens it undercuts Step 3.7 Flash, 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.
A team with data-privacy or self-hosting needs: Step 3.7 Flash — Open weights let you run it on your own hardware; GPT-4o mini is API-only.
Anyone whose priority is very low cost per token for its capability tier: GPT-4o mini — 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.
An enterprise with regional data-residency rules: GPT-4o mini or Step 3.7 Flash — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GPT-4o mini: where it fits
OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. Released July 18, 2024 by OpenAI, it is built for very low cost per token for its capability tier, strong coding for a small model (87.2% HumanEval), leading MMLU among peer small models (82%), and text and image (vision) understanding in the API.
Its trade-offs are real: only 128K context with an October 2023 knowledge cutoff, and weaker on hard reasoning and coding than frontier models. At $0.15 in / $0.6 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. GPT-4o mini 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.
Frequently asked questions
Is GPT-4o mini 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, GPT-4o mini leans toward very low cost per token for its capability tier 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, GPT-4o mini 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 GPT-4o mini is API-metered at $0.15/$0.6 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?
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 GPT-4o mini and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you GPT-4o mini, 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, GPT-4o mini or Step 3.7 Flash?
Step 3.7 Flash — released May 29, 2026, about 23 months after GPT-4o mini.
GPT-4o mini vs Step 3.7 Flash
OpenAI · US | StepFun · China · Updated June 2026
Quick verdict
Pick GPT-4o mini for very low cost per token for its capability tier or strong coding for a small model (87.2% humaneval). 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; GPT-4o mini if you want a managed API.
GPT-4o mini (OpenAI, US) and Step 3.7 Flash (StepFun, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GPT-4o mini is openAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. 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, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: GPT-4o mini is about 1.3× cheaper on input ($0.15/$0.6 per 1M tokens vs $0.2/$1.15 per 1M tokens) — modest, but it adds up at steady volume.
▸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 23 months (released May 29, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
GPT-4o mini
Step 3.7 Flash
Provider
OpenAI (US)
StepFun (China)
Released
July 18, 2024
May 29, 2026
Context window
128K (~192 pages)
256K (~393 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Very low cost per token for its capability tier
GPT-4o mini
At $0.15/$0.6 per 1M tokens it undercuts Step 3.7 Flash ($0.2/$1.15 per 1M tokens), and that gap compounds at volume.
Strong coding for a small model (87.2% HumanEval)
GPT-4o mini
Step 3.7 Flash is comparatively weak here — smaller ecosystem and less third-party documentation than the more established Chinese labs
Leading MMLU among peer small models (82%)
GPT-4o mini
OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch — and it runs cheaper at $0.15/$0.6 per 1M tokens.
A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows
Step 3.7 Flash
GPT-4o mini is comparatively weak here — weaker on hard reasoning and coding than frontier models
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 carries the larger 256K context.
Open weights (Apache 2.0) at a low per-token price
Step 3.7 Flash
Its 256K window holds about 2× more than GPT-4o mini's 128K in a single prompt.
Lowest cost at scale
GPT-4o mini
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
Step 3.7 Flash
Its 256K window is about 2× larger than GPT-4o mini's 128K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GPT-4o mini
At $0.15/$0.6 per 1M tokens it undercuts Step 3.7 Flash, 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.
A team with data-privacy or self-hosting needs
→ Step 3.7 Flash
Open weights let you run it on your own hardware; GPT-4o mini is API-only.
Anyone whose priority is very low cost per token for its capability tier
→ GPT-4o mini
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.
An enterprise with regional data-residency rules
→ GPT-4o mini or Step 3.7 Flash
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GPT-4o mini: where it fits
OpenAI's budget small multimodal model — cheap, fast text-and-vision intelligence that outscored peer small models like Gemini 1.5 Flash and Claude 3 Haiku on MMLU and HumanEval at launch. Released July 18, 2024 by OpenAI, it is built for very low cost per token for its capability tier, strong coding for a small model (87.2% HumanEval), leading MMLU among peer small models (82%), and text and image (vision) understanding in the API.
Its trade-offs are real: only 128K context with an October 2023 knowledge cutoff, and weaker on hard reasoning and coding than frontier models. At $0.15 in / $0.6 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. GPT-4o mini 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 GPT-4o mini 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 GPT-4o mini 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, GPT-4o mini leans toward very low cost per token for its capability tier 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, GPT-4o mini 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 GPT-4o mini is API-metered at $0.15/$0.6 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?
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 GPT-4o mini and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you GPT-4o mini, 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, GPT-4o mini or Step 3.7 Flash?
Step 3.7 Flash — released May 29, 2026, about 23 months after GPT-4o mini.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.