Compare/Grok 4.6 (low) vs Qwen3.8 2.4T A95B

Grok 4.6 (low)vsQwen3.8 2.4T A95B

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

SpaceXAI

Grok 4.6 (low)

Input
$2/M
Output
$6/M
Speed
55 tok/s
TTFT
8.05s
Alibaba

Qwen3.8 2.4T A95B

Input
$2/M
Output
$6/M
Speed
40 tok/s
TTFT
2.64s

Winner by Category

Cheaper
Tie
Faster (tok/s)
Grok 4.6 (low)
Lower Latency
Qwen3.8 2.4T A95B
Benchmarks (0-2)
Qwen3.8 2.4T A95B

Pricing Comparison

MetricGrok 4.6 (low)Qwen3.8 2.4T A95B
Input ($/M tokens)$2$2
Output ($/M tokens)$6$6
Cost for 1M input + 100K output tokens:
Grok 4.6 (low)$2.60
Qwen3.8 2.4T A95B$2.60

Speed Comparison

Output Speed (tokens/s) — higher is better
Grok 4.6 (low)
55 tok/s
Qwen3.8 2.4T A95B
40 tok/s
Time to First Token (seconds) — lower is better
Grok 4.6 (low)
8.05s
Qwen3.8 2.4T A95B
2.64s

Editorial Analysis

Verdict. Qwen3.8 2.4T A95B 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 mid-tier bracket for output-token pricing. At 1.0× the per-million-token cost, Qwen3.8 2.4T A95B is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3.8 2.4T A95B makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Grok 4.6 (low) is strongest on Coding Index (66.3), Intelligence Index (51.7). Qwen3.8 2.4T A95B leads on Coding Index (71.9), Intelligence Index (57.7).

Speed. On throughput, Grok 4.6 (low) generates tokens at 55 tok/s versus 40 tok/s — about 28% faster. On time-to-first-token, Qwen3.8 2.4T A95B responds in 2640ms vs 8050ms, which matters most for chat-style UIs.

Provider. SpaceXAI and Alibaba sell to overlapping but distinct developer audiences: SpaceXAI tends to ship frontier reasoning models with premium positioning, while Alibaba 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): Grok 4.6 (low) costs $150.00 ($1800/year); Qwen3.8 2.4T A95B costs $150.00 ($1800/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Grok 4.6 (low) ≈ $22.00/run, Qwen3.8 2.4T A95B ≈ $22.00/run. At agent/realtime scale (200M input / 100M output per million requests): Grok 4.6 (low) ≈ $1000/run, Qwen3.8 2.4T A95B ≈ $1000/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

  • Aggregate benchmark score (sum across 12 categories, capped at 100): Grok 4.6 (low) = 118, Qwen3.8 2.4T A95B = 130. Within 15% — effectively equivalent if both meet the threshold your product requires.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
51.757.7
Coding Index
66.371.9
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Grok 4.6 (low)0 wins
2 winsQwen3.8 2.4T A95B

Frequently Asked Questions

Which is cheaper, Grok 4.6 (low) or Qwen3.8 2.4T A95B?

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

Which model performs better on benchmarks?

Qwen3.8 2.4T A95B wins 2 out of 12 benchmarks compared to 0 for Grok 4.6 (low). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Grok 4.6 (low) generates tokens faster at 55 tok/s vs 40 tok/s. However, Qwen3.8 2.4T A95B has lower time-to-first-token (2.64s vs 8.05s).

When should I use Grok 4.6 (low) vs Qwen3.8 2.4T A95B?

Choose based on your priorities: both are similarly priced, Qwen3.8 2.4T A95B for stronger benchmark performance, and Grok 4.6 (low) for faster generation. For latency-sensitive apps, check the TTFT comparison above.