Compare / Spark-X2.6 4B vs Qwen3 4B Thinking 2507

Spark-X2.6 4B vs Qwen3 4B Thinking 2507

Updated November 2026

Verdict

Spark-X2.6 4B scores higher on the Small LM Index: 67.5 vs 55.7. On the 2 benchmarks both labs report, Spark-X2.6 4B wins 2 and Qwen3 4B Thinking 2507 wins 0. Both need about 4.2 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.

Pick Spark-X2.6 4B if…

you want the strongest model overall
you need math and reasoning
you work with long documents

Pick Qwen3 4B Thinking 2507 if…

you want the more widely used model (more quantized builds and community support)

Specifications

	Spark-X2.6 4B	Qwen3 4B Thinking 2507
Small LM Index	67.5 (likely 65–70)	55.7 (likely 51–60)
Lab	SparkLLM (XHToken) · China	Alibaba (Qwen) · China
Released	2026-10-19	2025-08-06
Parameters	4.1B (dense)	4B (dense)
Memory at 4-bit	~3.4 GB	~4.2 GB
Context window	1M tokens	262K tokens
Input	text	text
Reasoning	Hybrid (thinking on/off)	Always thinks
License	Apache-2.0	Apache-2.0
API price / 1M tokens	Self-host only	Self-host only
Hugging Face downloads (30 days)	18.9K	440.9K

Benchmarks

Higher is better. Bold marks the winner where both labs report a score. ≈ marks the index model's estimate for a score that wasn't reported, ✓ an independent measurement and * a score measured by another lab.

Benchmark	Spark-X2.6 4B	Qwen3 4B Thinking 2507
MMLU-Pro Knowledge	≈ 83	74.0
GPQA Diamond Knowledge	77.1	65.8
Humanity's Last Exam Reasoning	17.9	≈ 8
AIME Math	91.2	81.3
LiveCodeBench Coding	≈ 78	55.2
SWE-bench Verified Coding	62.8	≈ 39
τ²-Bench Agentic	86.9	—
IFBench Instruction following	75.8	—
IFEval Instruction following	93.6	—
BFCL Agentic	71.8	—
HMMT Math	81.3	—
AA-LCR Long context	67.3	—
SciCode Coding	36.8	—

Scores are mostly reported by each lab with its own settings, so small gaps are noise. See the methodology. Open in the interactive comparison to add more models.

Questions

Which is better, Spark-X2.6 4B or Qwen3 4B Thinking 2507?

Spark-X2.6 4B scores higher on the Small LM Index: 67.5 vs 55.7. On the 2 benchmarks both labs report, Spark-X2.6 4B wins 2 and Qwen3 4B Thinking 2507 wins 0.

Which is cheaper to run?

Neither model has a hosted API on OpenRouter, so you would run both yourself. Both need about 4.2 GB of memory at 4-bit quantization.

Which has the longer context window?

Spark-X2.6 4B has the longer context window: 1M tokens against 262K.

Can I use Spark-X2.6 4B and Qwen3 4B Thinking 2507 commercially?

Both are released under the Apache-2.0 license. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.