Model comparison

GPT-5.4 Nano vs Qwen 3.8 27B

OpenAI against Qwen, compared on context, price, and verified benchmark results.

Catalog record checked September 26, 2026Individual provider fields may changeEvidence confidence: High — see receipts below

OpenAI

GPT-5.4 Nano

Budget

Qwen

Qwen 3.8 27B

Budget · Open weights

AI model capability comparison
Specification	GPT-5.4 Nano	Qwen 3.8 27B
Provider
Provider	OpenAI	Qwen
Tier
Tier	Budget	Budget
Context window
Context window	Winner: 400K	262K
Max output
Max output	128K	Not verifiedUnverified
Input / 1M tokens
Input / 1M tokens	Winner: $0.20	$0.50
Output / 1M tokens
Output / 1M tokens	Winner: $1.25	$3
Weights
Weights	Closed	Open
Parameters
Parameters	Not disclosedUnverified	27B dense VLM (Gated DeltaNet hybrid)
Reasoning levels
Reasoning levels	none, low, medium, high, xhigh	low, medium, xhigh
Modalities
Modalities	text, image	text, image, video
License
License	Not disclosedUnverified	Apache 2.0
API model id
API model id	gpt-5.4-nano	qwen3.8-27b
Released
Released	March 17, 2026	August 13, 2026
Artificial Analysis Intelligence Index [xhigh] (2026-09-26)
Artificial Analysis Intelligence Index [xhigh] (2026-09-26)	20.7	Winner: 33.7

Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

2026-09-26
Artificial Analysis
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check measuredAt.

Pricing tiers

GPT-5.4 Nano: Standard $0.20/$1.25 per MTok; cached input $0.02/MTok; batch $0.10/$0.625.

Qwen 3.8 27B: Hosted qwen3.8-27b lists $0.50/$3.00 per MTok on Alibaba Cloud Model Studio (API launched 2026-08-19). Open weights (Apache 2.0) for self-hosting.

BudgetRecord checked September 26, 2026

GPT-5.4 Nano

GPT-5.4 Nano is OpenAI's small classification and extraction tier at $0.20/$1.25 — no longer its cheapest model (GPT-6 Luna lists $0.10/$0.50).

Best for

Classification
Summarisation
High-volume chat

Watch out

Smallest tier; not for hard reasoning or coding.

BudgetOpen weightsRecord checked September 26, 2026

Qwen 3.8 27B

Qwen 3.8 27B is Alibaba's compact deployment-friendly dense VLM — 262K native context (1M via YaRN), Apache 2.0 weights, distinct from the hosted Qwen 3.8 Max API.

Best for

Self-hosting on a single node
Multimodal input at small scale
Long-context on modest hardware

Watch out

Dense 27B means higher memory per token than an MoE of equal active size; video input is multimodal-input only.

When the cheaper one wins

GPT-5.4 Nano is cheaper on output at $1.25 per million tokens against $3 for Qwen 3.8 27B — about 2.4×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.

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Evidence confidence: High

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

Pricing verified on both sides — Input and output rates are verified for both models.
3/5 core specs verified on both sides — Not published for at least one side: max output, parameter count.
1 shared named benchmark with differing scores — Measured on: Artificial Analysis Intelligence Index.
Verified within the last 90 days — Newest catalog check was 2 days ago.
Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

GPT-5.4 Nano: OpenAI — GPT-5.4 Mini & Nano (accessed 2026-08-29)
GPT-5.4 Nano: OpenAI API pricing (gpt-5.4-nano $0.20/$1.25) (accessed 2026-08-29)
Qwen 3.8 27B: HuggingFace — Qwen3.8-27B model card (accessed 2026-08-29)
Qwen 3.8 27B: LiveBench Leaderboard (Qwen 3.8 27B row) (accessed 2026-08-29)
Qwen 3.8 27B: Alibaba Cloud Model Studio pricing (qwen3.8-27b $0.50/$3.00) (accessed 2026-09-26)

Related comparisons

Diving deeper on one model? GPT-5.4 Nano · Qwen 3.8 27B

Common questions

GPT-5.4 Nano vs Qwen 3.8 27B

Answered from the verified figures on this page rather than general guidance.

Is GPT-5.4 Nano or Qwen 3.8 27B cheaper for input?

GPT-5.4 Nano is cheaper at $0.20 per million input tokens, against $0.50 for Qwen 3.8 27B — roughly 2.5× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.4 Nano has tiered pricing: Standard $0.20/$1.25 per MTok; cached input $0.02/MTok; batch $0.10/$0.625. Qwen 3.8 27B has tiered pricing: Hosted qwen3.8-27b lists $0.50/$3.00 per MTok on Alibaba Cloud Model Studio (API launched 2026-08-19). Open weights (Apache 2.0) for self-hosting.

Is GPT-5.4 Nano or Qwen 3.8 27B cheaper for output?

GPT-5.4 Nano is cheaper at $1.25 per million output tokens, against $3 for Qwen 3.8 27B — roughly 2.4× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.4 Nano has tiered pricing: Standard $0.20/$1.25 per MTok; cached input $0.02/MTok; batch $0.10/$0.625. Qwen 3.8 27B has tiered pricing: Hosted qwen3.8-27b lists $0.50/$3.00 per MTok on Alibaba Cloud Model Studio (API launched 2026-08-19). Open weights (Apache 2.0) for self-hosting.

Which has the larger context window, GPT-5.4 Nano or Qwen 3.8 27B?

GPT-5.4 Nano accepts 400K tokens against 262K for Qwen 3.8 27B. This only matters if you routinely send very long documents or large codebases.

Do GPT-5.4 Nano and Qwen 3.8 27B support the same reasoning levels?

GPT-5.4 Nano exposes none, low, medium, high, xhigh, while Qwen 3.8 27B exposes low, medium, xhigh.

Should I use GPT-5.4 Nano or Qwen 3.8 27B?

Both sit in the budget tier, so the choice usually comes down to price and context rather than capability. GPT-5.4 Nano suits classification; Qwen 3.8 27B suits self-hosting on a single node.

Can I self-host GPT-5.4 Nano or Qwen 3.8 27B?

Qwen 3.8 27B publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. GPT-5.4 Nano is a closed model whose supported access paths are controlled by its provider.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

Input price: GPT-5.4 Nano costs $0.20 per 1M tokens versus $0.50 for Qwen 3.8 27B — a 2.5x difference at the headline tier.
Context: GPT-5.4 Nano takes 400K against 262K for Qwen 3.8 27B — only decisive if your prompts approach the smaller window.
Measured capability: Qwen 3.8 27B leads Artificial Analysis Intelligence Index 33.7 to 20.7 (measured 2026-09-26).
Deployment: Qwen 3.8 27B publishes weights you can self-host; the other is API-only.

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