💰 Unit Cost Winner Qwen 3.8 27B $0.20/1M in · $0.60/1M out
⚡ Latency & Speed Winner Qwen 3.8 27B 120ms TTFT · 140 tok/s
💻 Coding (SWE) Winner Grok 4 73.2% SWE-bench · 95% HumanEval
🧠 STEM Reasoning Winner Grok 4 86.2% GPQA · 97.6% MATH-500

Which model is better: Grok 4 or Qwen 3.8 27B?

Qwen 3.8 27B is 92.0% cheaper for input tokens ($0.20 vs. $2.50 per 1M tokens) and $0.60 vs. $12.50 for output tokens (12.5x cost advantage). In terms of operational performance, Qwen 3.8 27B delivers lower response latency at 120ms TTFT (360ms faster than Grok 4) with 140 vs. 95 tok/s throughput. Grok 4 leads coding evaluations at 73.2% SWE-bench Verified vs. 58.4% for Qwen 3.8 27B and 86.2% vs. 68.0% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.

Verified Performance & Unit Economics Delta (12 Dimensions)

Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.

Metric / Dimension Grok 4 Qwen 3.8 27B Net Delta / Advantage
Input Cost / 1M Tokens $2.50 $0.20 Qwen 3.8 27B is 12.5x cheaper
Output Cost / 1M Tokens $12.50 $0.60 Qwen 3.8 27B (95.2% lower)
Prompt Cache Read / 1M $0.500 $0.050 Qwen 3.8 27B is 10.0x cheaper
Prompt Cache Write / 1M $2.50 $0.20 Qwen 3.8 27B is 12.5x cheaper
Context Window (Max Output) 262k (66k out) 131k (16k out) Grok 4 (2.0x larger context)
Avg TTFT Response Latency 480ms 120ms Qwen 3.8 27B (360ms faster)
Generation Speed (Throughput) 95 tok/s 140 tok/s Qwen 3.8 27B (+45 tok/s faster)
SWE-bench Verified (Coding) 73.2% 58.4% Grok 4 (+14.8% lead)
GPQA Diamond (PhD STEM) 86.2% 68% Grok 4 (+18.2% lead)
MMLU-Pro (Multi-Discipline) 89.9% 81.2% Grok 4 (+8.7% lead)
MATH-500 (Quantitative Logic) 97.6% 86.4% Grok 4 (+11.2% lead)
Inference Value Score (IVR) 57.6 / 100 74.9 / 100 Qwen 3.8 27B (+17.3 pts)

Interactive Monthly Token Economics & ROI Forecaster

Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.

25.0M Tokens
500K 50M 250M 500M+
5.0M Tokens
100K 25M 75M 150M+
50% Cached
0% (Uncached) 25% 50% (Typical Agent) 95% Max

Estimated Monthly Spend

Grok 4 $100.00

$2.50/1M in · $12.50/1M out

Prompt Cache: $0.500/1M read · $2.50/1M write

Qwen 3.8 27B $6.13

$0.20/1M in · $0.60/1M out

Prompt Cache: $0.050/1M read · $0.20/1M write

Projected Monthly Cost Reduction
$93.88 / mo
(93.9% lower cost with Qwen 3.8 27B)
Open Full Matchup Analysis →
A

When to Choose Grok 4

Choose Grok 4 (xAI) for real-time financial/market intelligence, PhD-level STEM problem solving, and autonomous code generation — offering a 262k context window, 480ms TTFT, and $2.50/1M input pricing ($0.500/1M cached reads).

B

When to Choose Qwen 3.8 27B

Choose Qwen 3.8 27B (Alibaba Cloud) for high-performance 27B open weights optimized for local deployment, code generation, and low-latency production pipelines — offering a 131k context window, 120ms TTFT, and $0.20/1M input pricing ($0.050/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: Grok 4 or Qwen 3.8 27B?

Qwen 3.8 27B is cheaper at $0.20/1M input tokens ($0.60/1M output) compared to $2.50/1M input ($12.50/1M output) for Grok 4.

Which model has lower TTFT latency and higher tok/s throughput: Grok 4 or Qwen 3.8 27B?

Qwen 3.8 27B delivers faster first-token latency at 120ms TTFT (140 tok/s throughput) compared to 480ms TTFT (95 tok/s) for Grok 4.

How do Grok 4 and Qwen 3.8 27B compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, Grok 4 scores 73.2% versus 58.4% for Qwen 3.8 27B. On GPQA Diamond graduate-level STEM reasoning, Grok 4 scores 86.2% versus 68% for Qwen 3.8 27B (and 97.6% vs 86.4% on MATH-500).

When should you choose Grok 4?

Choose Grok 4 (xAI) for real-time financial/market intelligence, PhD-level STEM problem solving, and autonomous code generation — offering a 262k context window, 480ms TTFT, and $2.50/1M input pricing ($0.500/1M cached reads).

When should you choose Qwen 3.8 27B?

Choose Qwen 3.8 27B (Alibaba Cloud) for high-performance 27B open weights optimized for local deployment, code generation, and low-latency production pipelines — offering a 131k context window, 120ms TTFT, and $0.20/1M input pricing ($0.050/1M cached reads).

Explore High-Priority Related Model Matchups