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 Qwen 3.7 Plus for reading screens and interacting with guis or generating code from visual references. On a tight budget at scale, Qwen 3.7 Plus is the value pick.
Grok 4.5 (xAI, US) and Qwen 3.7 Plus (Alibaba, 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. Qwen 3.7 Plus is alibaba's cost-effective multimodal agent in the Qwen3.7 series, built to perceive scenes, read screens and GUIs, generate code from visual references, and navigate mobile apps end-to-end. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: Qwen 3.7 Plus is about 5× cheaper on input ($0.4/$1.6 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: Qwen 3.7 Plus holds 2× more — 1M (~1,500 pages) vs 500K (~750 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 37 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
Qwen 3.7 Plus
Provider
xAI (US)
Alibaba (China)
Released
July 8, 2026
June 1, 2026
Context window
500K (~750 pages)
1M (~1,500 pages)
Price (in/out)
$2/$6 per 1M tokens
$0.4/$1.6 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image, code
text, image, video, 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 — A core design strength of Grok 4.5.
Extreme token efficiency — around 4x fewer output tokens per task than Opus 4.8: Grok 4.5 — A core design strength of Grok 4.5.
In-IDE coding, trained on real Cursor developer sessions and shipped natively in Cursor: Grok 4.5 — A core design strength of Grok 4.5.
Reading screens and interacting with GUIs: Qwen 3.7 Plus — A core design strength of Qwen 3.7 Plus.
Generating code from visual references: Qwen 3.7 Plus — A core design strength of Qwen 3.7 Plus.
Agentic tool use, verification, and autonomous iteration: Qwen 3.7 Plus — A core design strength of Qwen 3.7 Plus.
Lowest cost at scale: Qwen 3.7 Plus — At $0.4/$1.6 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Qwen 3.7 Plus — Its 1M window is about 2× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Qwen 3.7 Plus — At $0.4/$1.6 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: Qwen 3.7 Plus — Larger 1M window fits more in one prompt.
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 reading screens and interacting with guis: Qwen 3.7 Plus — That is its strongest area.
An enterprise with regional data-residency rules: Grok 4.5 or Qwen 3.7 Plus — 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.
Qwen 3.7 Plus: where it fits
Alibaba's cost-effective multimodal agent in the Qwen3.7 series, built to perceive scenes, read screens and GUIs, generate code from visual references, and navigate mobile apps end-to-end. Released June 1, 2026 by Alibaba, it is built for reading screens and interacting with GUIs, generating code from visual references, agentic tool use, verification, and autonomous iteration, and cost-effective vision-language processing at 1M context.
Its trade-offs: proprietary and API-only, with no downloadable weights, and outputs text only, no image, audio, or video generation. At $0.4 in / $1.6 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Grok 4.5 (US) and Qwen 3.7 Plus (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Qwen 3.7 Plus is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Frequently asked questions
Is Grok 4.5 or Qwen 3.7 Plus 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 Qwen 3.7 Plus leans toward reading screens and interacting with guis, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Grok 4.5 or Qwen 3.7 Plus?
Qwen 3.7 Plus is cheaper — $2/$6 per 1M tokens vs $0.4/$1.6 per 1M tokens, roughly 5× apart on input.
Which has the bigger context window?
Qwen 3.7 Plus — 1M vs 500K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Grok 4.5 and Qwen 3.7 Plus together?
Yes — a multi-model platform like LumiChats gives you Grok 4.5, Qwen 3.7 Plus 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 Qwen 3.7 Plus?
Grok 4.5 — released July 8, 2026, about 37 days after Qwen 3.7 Plus.
Grok 4.5 vs Qwen 3.7 Plus
xAI · US | Alibaba · 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 Qwen 3.7 Plus for reading screens and interacting with guis or generating code from visual references. On a tight budget at scale, Qwen 3.7 Plus is the value pick.
Grok 4.5 (xAI, US) and Qwen 3.7 Plus (Alibaba, 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. Qwen 3.7 Plus is alibaba's cost-effective multimodal agent in the Qwen3.7 series, built to perceive scenes, read screens and GUIs, generate code from visual references, and navigate mobile apps end-to-end. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Qwen 3.7 Plus is about 5× cheaper on input ($0.4/$1.6 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: Qwen 3.7 Plus holds 2× more — 1M (~1,500 pages) vs 500K (~750 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 37 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
Qwen 3.7 Plus
Provider
xAI (US)
Alibaba (China)
Released
July 8, 2026
June 1, 2026
Context window
500K (~750 pages)
1M (~1,500 pages)
Price (in/out)
$2/$6 per 1M tokens
$0.4/$1.6 per 1M tokens
Open weight?
No — API only
No — API only
Modalities
text, image, code
text, image, video, 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
A core design strength of Grok 4.5.
Extreme token efficiency — around 4x fewer output tokens per task than Opus 4.8
Grok 4.5
A core design strength of Grok 4.5.
In-IDE coding, trained on real Cursor developer sessions and shipped natively in Cursor
Grok 4.5
A core design strength of Grok 4.5.
Reading screens and interacting with GUIs
Qwen 3.7 Plus
A core design strength of Qwen 3.7 Plus.
Generating code from visual references
Qwen 3.7 Plus
A core design strength of Qwen 3.7 Plus.
Agentic tool use, verification, and autonomous iteration
Qwen 3.7 Plus
A core design strength of Qwen 3.7 Plus.
Lowest cost at scale
Qwen 3.7 Plus
At $0.4/$1.6 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Qwen 3.7 Plus
Its 1M window is about 2× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Qwen 3.7 Plus
At $0.4/$1.6 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
→ Qwen 3.7 Plus
Larger 1M window fits more in one prompt.
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 reading screens and interacting with guis
→ Qwen 3.7 Plus
That is its strongest area.
An enterprise with regional data-residency rules
→ Grok 4.5 or Qwen 3.7 Plus
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.
Qwen 3.7 Plus: where it fits
Alibaba's cost-effective multimodal agent in the Qwen3.7 series, built to perceive scenes, read screens and GUIs, generate code from visual references, and navigate mobile apps end-to-end. Released June 1, 2026 by Alibaba, it is built for reading screens and interacting with GUIs, generating code from visual references, agentic tool use, verification, and autonomous iteration, and cost-effective vision-language processing at 1M context.
Its trade-offs: proprietary and API-only, with no downloadable weights, and outputs text only, no image, audio, or video generation. At $0.4 in / $1.6 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Grok 4.5 (US) and Qwen 3.7 Plus (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Qwen 3.7 Plus is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both Grok 4.5 and Qwen 3.7 Plus 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 Qwen 3.7 Plus leans toward reading screens and interacting with guis, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Grok 4.5 or Qwen 3.7 Plus?
Qwen 3.7 Plus is cheaper — $2/$6 per 1M tokens vs $0.4/$1.6 per 1M tokens, roughly 5× apart on input.
Which has the bigger context window?
Qwen 3.7 Plus — 1M vs 500K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Grok 4.5 and Qwen 3.7 Plus together?
Yes — a multi-model platform like LumiChats gives you Grok 4.5, Qwen 3.7 Plus 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 Qwen 3.7 Plus?
Grok 4.5 — released July 8, 2026, about 37 days after Qwen 3.7 Plus.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.