Pick Grok 4.5 for cheap, token-efficient agentic coding — about gpt-5.5-codex quality at roughly half the cost or extreme token efficiency — around 4x fewer output tokens per task than opus 4.8. 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; Grok 4.5 if you want a managed API.
Grok 4.5 (xAI, 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. Grok 4.5 is xAI's first coding-focused model — pitched as Opus-class but faster, more token-efficient, and cheaper, undercutting GPT-5.5-Codex. 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: Step 3.7 Flash is about 10× cheaper on input ($0.2/$1.15 per 1M tokens vs $2/$6 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Grok 4.5 holds 1.9× more — 500K (~750 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: Grok 4.5 is the newer model by about 40 days (released July 8, 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
Grok 4.5
Step 3.7 Flash
Provider
xAI (US)
StepFun (China)
Released
July 8, 2026
May 29, 2026
Context window
500K (~750 pages)
256K (~393 pages)
Price (in/out)
$2/$6 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
No — API only
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
Cheap, token-efficient agentic coding — about GPT-5.5-Codex quality at roughly half the cost: Grok 4.5 — Its 500K window holds about 1.9× more than Step 3.7 Flash's 256K in a single prompt.
Extreme token efficiency — around 4x fewer output tokens per task than Opus 4.8: Grok 4.5 — XAI's first coding-focused model — pitched as Opus-class but faster, more token-efficient, and cheaper, undercutting GPT-5.5-Codex — and it carries the larger 500K context.
In-IDE coding, trained on real Cursor developer sessions and shipped natively in Cursor: Grok 4.5 — XAI's first coding-focused model — pitched as Opus-class but faster, more token-efficient, and cheaper, undercutting GPT-5.5-Codex — and it is the newer of the two.
A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows: 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.
SCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size: Step 3.7 Flash — Grok 4.5 is comparatively weak here — smaller 500K context (halved from the 1M generation), with pricing that doubles above 200K 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 Grok 4.5 ($2/$6 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: Grok 4.5 — Its 500K window is about 1.9× larger than Step 3.7 Flash's 256K, fitting roughly 750 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 Grok 4.5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Grok 4.5 — Larger 500K 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; Grok 4.5 is API-only.
Anyone whose priority is cheap, token-efficient agentic coding — about gpt-5.5-codex quality at roughly half the cost: Grok 4.5 — 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.
An enterprise with regional data-residency rules: Grok 4.5 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.
Grok 4.5: where it fits
XAI's first coding-focused model — pitched as Opus-class but faster, more token-efficient, and cheaper, undercutting GPT-5.5-Codex. Released July 8, 2026 by xAI, it is built for cheap, token-efficient agentic coding — about GPT-5.5-Codex quality at roughly half the cost, extreme token efficiency — around 4x fewer output tokens per task than Opus 4.8, in-IDE coding, trained on real Cursor developer sessions and shipped natively in Cursor, and top-tier placement on the Artificial Analysis Intelligence Index.
Its trade-offs are real: smaller 500K context (halved from the 1M generation), with pricing that doubles above 200K tokens, and eU launch delayed; no open weights. At $2 in / $6 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
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. Grok 4.5 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 Grok 4.5 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, Grok 4.5 leans toward cheap, token-efficient agentic coding — about gpt-5.5-codex quality at roughly half the cost 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, Grok 4.5 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 Grok 4.5 is API-metered at $2/$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?
Grok 4.5 — 500K vs 256K, about 1.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Grok 4.5 and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you Grok 4.5, 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, Grok 4.5 or Step 3.7 Flash?
Grok 4.5 — released July 8, 2026, about 40 days after Step 3.7 Flash.
Grok 4.5 vs Step 3.7 Flash
xAI · US | StepFun · China · Updated June 2026
Quick verdict
Pick Grok 4.5 for cheap, token-efficient agentic coding — about gpt-5.5-codex quality at roughly half the cost or extreme token efficiency — around 4x fewer output tokens per task than opus 4.8. 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; Grok 4.5 if you want a managed API.
Grok 4.5 (xAI, 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. Grok 4.5 is xAI's first coding-focused model — pitched as Opus-class but faster, more token-efficient, and cheaper, undercutting GPT-5.5-Codex. 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: Step 3.7 Flash is about 10× cheaper on input ($0.2/$1.15 per 1M tokens vs $2/$6 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Grok 4.5 holds 1.9× more — 500K (~750 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: Grok 4.5 is the newer model by about 40 days (released July 8, 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
Grok 4.5
Step 3.7 Flash
Provider
xAI (US)
StepFun (China)
Released
July 8, 2026
May 29, 2026
Context window
500K (~750 pages)
256K (~393 pages)
Price (in/out)
$2/$6 per 1M tokens
$0.2/$1.15 per 1M tokens
Open weight?
No — API only
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
Cheap, token-efficient agentic coding — about GPT-5.5-Codex quality at roughly half the cost
Grok 4.5
Its 500K window holds about 1.9× more than Step 3.7 Flash's 256K in a single prompt.
Extreme token efficiency — around 4x fewer output tokens per task than Opus 4.8
Grok 4.5
XAI's first coding-focused model — pitched as Opus-class but faster, more token-efficient, and cheaper, undercutting GPT-5.5-Codex — and it carries the larger 500K context.
In-IDE coding, trained on real Cursor developer sessions and shipped natively in Cursor
Grok 4.5
XAI's first coding-focused model — pitched as Opus-class but faster, more token-efficient, and cheaper, undercutting GPT-5.5-Codex — and it is the newer of the two.
A 198B-parameter sparse MoE vision-language model (11B active) built specifically for agentic coding and search workflows
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.
SCMP reported it 'outshines larger rivals' from DeepSeek and Moonshot on some benchmarks despite its smaller active size
Step 3.7 Flash
Grok 4.5 is comparatively weak here — smaller 500K context (halved from the 1M generation), with pricing that doubles above 200K 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 Grok 4.5 ($2/$6 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
Grok 4.5
Its 500K window is about 1.9× larger than Step 3.7 Flash's 256K, fitting roughly 750 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 Grok 4.5, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Grok 4.5
Larger 500K 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; Grok 4.5 is API-only.
Anyone whose priority is cheap, token-efficient agentic coding — about gpt-5.5-codex quality at roughly half the cost
→ Grok 4.5
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.
An enterprise with regional data-residency rules
→ Grok 4.5 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.
Grok 4.5: where it fits
XAI's first coding-focused model — pitched as Opus-class but faster, more token-efficient, and cheaper, undercutting GPT-5.5-Codex. Released July 8, 2026 by xAI, it is built for cheap, token-efficient agentic coding — about GPT-5.5-Codex quality at roughly half the cost, extreme token efficiency — around 4x fewer output tokens per task than Opus 4.8, in-IDE coding, trained on real Cursor developer sessions and shipped natively in Cursor, and top-tier placement on the Artificial Analysis Intelligence Index.
Its trade-offs are real: smaller 500K context (halved from the 1M generation), with pricing that doubles above 200K tokens, and eU launch delayed; no open weights. At $2 in / $6 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
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. Grok 4.5 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 Grok 4.5 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, Grok 4.5 leans toward cheap, token-efficient agentic coding — about gpt-5.5-codex quality at roughly half the cost 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, Grok 4.5 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 Grok 4.5 is API-metered at $2/$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?
Grok 4.5 — 500K vs 256K, about 1.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Grok 4.5 and Step 3.7 Flash together?
Yes — a multi-model platform like LumiChats gives you Grok 4.5, 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, Grok 4.5 or Step 3.7 Flash?
Grok 4.5 — released July 8, 2026, about 40 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.