Compare/Qwen3 VL 235B A22B Instruct vs GPT-4.1 mini

Qwen3 VL 235B A22B InstructvsGPT-4.1 mini

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

Alibaba

Qwen3 VL 235B A22B Instruct

Input
$0.4/M
Output
$1.6/M
Speed
47 tok/s
TTFT
2.70s
OpenAI

GPT-4.1 mini

Input
$0.4/M
Output
$1.6/M
Speed
86 tok/s
TTFT
0.84s

Winner by Category

Cheaper
Tie
Faster (tok/s)
GPT-4.1 mini
Lower Latency
GPT-4.1 mini
Benchmarks (0-2)
GPT-4.1 mini

Pricing Comparison

MetricQwen3 VL 235B A22B InstructGPT-4.1 mini
Input ($/M tokens)$0.4$0.4
Output ($/M tokens)$1.6$1.6
Cost for 1M input + 100K output tokens:
Qwen3 VL 235B A22B Instruct$0.56
GPT-4.1 mini$0.56

Speed Comparison

Output Speed (tokens/s) — higher is better
Qwen3 VL 235B A22B Instruct
47 tok/s
GPT-4.1 mini
86 tok/s
Time to First Token (seconds) — lower is better
Qwen3 VL 235B A22B Instruct
2.70s
GPT-4.1 mini
0.84s

Editorial Analysis

Verdict. GPT-4.1 mini takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.

Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, GPT-4.1 mini is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GPT-4.1 mini makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Qwen3 VL 235B A22B Instruct is strongest on Intelligence Index (14.4). GPT-4.1 mini leads on Coding Index (20.2), Intelligence Index (14.8).

Speed. On throughput, GPT-4.1 mini generates tokens at 86 tok/s versus 47 tok/s — about 45% faster. On time-to-first-token, GPT-4.1 mini responds in 840ms vs 2700ms, which matters most for chat-style UIs.

Provider. Alibaba and OpenAI sell to overlapping but distinct developer audiences: Alibaba tends to ship frontier reasoning models with premium positioning, while OpenAI often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.

Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Qwen3 VL 235B A22B Instruct costs $36.00 ($432/year); GPT-4.1 mini costs $36.00 ($432/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3 VL 235B A22B Instruct ≈ $5.20/run, GPT-4.1 mini ≈ $5.20/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3 VL 235B A22B Instruct ≈ $240/run, GPT-4.1 mini ≈ $240/run.

Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.

Head-to-head deltas

  • On throughput, GPT-4.1 mini is 1.83× faster (86 tok/s vs 47 tok/s). For streaming chat or real-time agents this alone often flips the recommendation.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
14.414.8
Coding Index
20.2
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Qwen3 VL 235B A22B Instruct0 wins
2 winsGPT-4.1 mini

Frequently Asked Questions

Which is cheaper, Qwen3 VL 235B A22B Instruct or GPT-4.1 mini?

Both models have similar pricing. Check the detailed breakdown above for input vs output token costs.

Which model performs better on benchmarks?

GPT-4.1 mini wins 2 out of 12 benchmarks compared to 0 for Qwen3 VL 235B A22B Instruct. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

GPT-4.1 mini generates tokens faster at 86 tok/s vs 47 tok/s. However, GPT-4.1 mini has lower time-to-first-token (0.84s vs 2.70s).

When should I use Qwen3 VL 235B A22B Instruct vs GPT-4.1 mini?

Choose based on your priorities: both are similarly priced, GPT-4.1 mini for stronger benchmark performance, and GPT-4.1 mini for faster generation. For latency-sensitive apps, check the TTFT comparison above.