Pick GPT-5.4 for strong general-purpose default or coding and software engineering. 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.
GPT-5.4 (OpenAI, 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. GPT-5.4 is openAI's 2026 workhorse — unifies Codex and GPT into a strong default that costs half of GPT-5.5. 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. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Price: Qwen 3.7 Plus is about 6.3× cheaper on input ($0.4/$1.6 per 1M tokens vs $2.5/$15 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Qwen 3.7 Plus is the newer model by about 3 months (released June 1, 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-5.4
Qwen 3.7 Plus
Provider
OpenAI (US)
Alibaba (China)
Released
March 5, 2026
June 1, 2026
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$2.5/$15 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
Strong general-purpose default: GPT-5.4 — A core design strength of GPT-5.4.
Coding and software engineering: GPT-5.4 — A core design strength of GPT-5.4.
Document understanding and tool use: GPT-5.4 — A core design strength of GPT-5.4.
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.
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 GPT-5.4, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is strong general-purpose default: GPT-5.4 — 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: GPT-5.4 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.
GPT-5.4: where it fits
OpenAI's 2026 workhorse — unifies Codex and GPT into a strong default that costs half of GPT-5.5. Released March 5, 2026 by OpenAI, it is built for strong general-purpose default, coding and software engineering, document understanding and tool use, and 1M context with good token efficiency.
Its trade-offs are real: topped by GPT-5.5 on the hardest tasks, and pricier than open-weight rivals. At $2.5 in / $15 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." GPT-5.4 (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 GPT-5.4 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, GPT-5.4 leans toward strong general-purpose default 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, GPT-5.4 or Qwen 3.7 Plus?
Qwen 3.7 Plus is cheaper — $2.5/$15 per 1M tokens vs $0.4/$1.6 per 1M tokens, roughly 6.3× apart on input.
Which has the bigger context window?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GPT-5.4 and Qwen 3.7 Plus together?
Yes — a multi-model platform like LumiChats gives you GPT-5.4, 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, GPT-5.4 or Qwen 3.7 Plus?
Qwen 3.7 Plus — released June 1, 2026, about 3 months after GPT-5.4.
GPT-5.4 vs Qwen 3.7 Plus
OpenAI · US | Alibaba · China · Updated June 2026
Quick verdict
Pick GPT-5.4 for strong general-purpose default or coding and software engineering. 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.
GPT-5.4 (OpenAI, 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. GPT-5.4 is openAI's 2026 workhorse — unifies Codex and GPT into a strong default that costs half of GPT-5.5. 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. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Price: Qwen 3.7 Plus is about 6.3× cheaper on input ($0.4/$1.6 per 1M tokens vs $2.5/$15 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Qwen 3.7 Plus is the newer model by about 3 months (released June 1, 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-5.4
Qwen 3.7 Plus
Provider
OpenAI (US)
Alibaba (China)
Released
March 5, 2026
June 1, 2026
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$2.5/$15 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
Strong general-purpose default
GPT-5.4
A core design strength of GPT-5.4.
Coding and software engineering
GPT-5.4
A core design strength of GPT-5.4.
Document understanding and tool use
GPT-5.4
A core design strength of GPT-5.4.
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.
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 GPT-5.4, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is strong general-purpose default
→ GPT-5.4
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
→ GPT-5.4 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.
GPT-5.4: where it fits
OpenAI's 2026 workhorse — unifies Codex and GPT into a strong default that costs half of GPT-5.5. Released March 5, 2026 by OpenAI, it is built for strong general-purpose default, coding and software engineering, document understanding and tool use, and 1M context with good token efficiency.
Its trade-offs are real: topped by GPT-5.5 on the hardest tasks, and pricier than open-weight rivals. At $2.5 in / $15 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." GPT-5.4 (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 GPT-5.4 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, GPT-5.4 leans toward strong general-purpose default 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, GPT-5.4 or Qwen 3.7 Plus?
Qwen 3.7 Plus is cheaper — $2.5/$15 per 1M tokens vs $0.4/$1.6 per 1M tokens, roughly 6.3× apart on input.
Which has the bigger context window?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both GPT-5.4 and Qwen 3.7 Plus together?
Yes — a multi-model platform like LumiChats gives you GPT-5.4, 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, GPT-5.4 or Qwen 3.7 Plus?
Qwen 3.7 Plus — released June 1, 2026, about 3 months after GPT-5.4.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.