Alibaba

Qwen3.8 Flash NextTerbuka

Alibaba Qwen3.8 Flash Next experimental high-throughput multimodal preview model.

#221dirilis 18 Agu 2026·Open Weights·Not disclosed
textimagevideo260K ctx
Penyedia APIDapatkan Kunci API →Peringkat
Inteligensi
66.8 #221
AAQI 61.5
Kecepatan output
168 t/s
TTFT 210 ms · ITL 6.0 ms/tok
Harga gabungan (3:1)
$0.19 /1M
Input $0.12/M · Output $0.4/M
Konteks
260K
≈ 650 halaman A4 · output maks 8,192

Quality Domain Radar

6-Axis Frontier Evaluation vs Category Average & Flagship

Qwen3.8 Flash Next
Claude Mythos Preview (#1)
Average (68)
Coding (60.5)Mathematics (68)Hard Science (GPQA) (66)Agentic & Tool Use (63.5)Knowledge (MMLU) (64)Vision / Multimodal (66)

Sumbu radar diaproksimasi dari skor kategori jika pengukuran benchmark langsung tidak tersedia — lihat Metodologi.

Kelebihan & Trade-off

Di mana ia menang

  • Open weights — self-host, fine-tune and audit with no vendor lock-in
  • 168 tok/s output — fast enough for real-time agents
  • Sub-400ms time-to-first-token
  • Very low blended price — $0.19/1M mixed tokens

Kasus Penggunaan Killer

Real-time agents
Low-latency loops for voice, gaming and interactive copilots.
High-volume batch jobs
Classification, extraction and summarization at minimal cost per call.
Vision & document OCR
Extracts structure from scans, charts, screenshots and mixed-media inputs.

Quickstart Developer & API

Atur DASHSCOPE_API_KEY dulu — dapatkan kunci API
curl https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
  -H "Authorization: Bearer $DASHSCOPE_API_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "qwen3-8-flash-next",
    "messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
  }'

Sumber Daya & Tautan Resmi

Deployment API

Bandingkan host & latensi

Throughput, TTFT, dan harga cache di Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock, dan Azure.

GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Lihat matriks penyedia →

Skor per sumber

Spesifikasi Live & Arsitektur

Di balik kap
  • Prompt caching supported — cached input reads at $0.02/1M tokens.
  • Native multimodality: text, image, video in a single model.
Tanggal rilis
18 Agu 2026
Parameter
Not disclosed
Lisensi
Open Weights
Jendela konteks
260,000 tokens
Output maks
8,192 tokens
Modalitas
text + image + video
Harga cache
baca $0.02 · tulis $0.16 /1M
Penalaran
—
Harga input
$0.12/M
Harga output
$0.4/M
Kecepatan output
168 t/s
Waktu ke token pertama
210 ms
Data diperbarui
28 Agu 2026

Estimasi tagihan bulanan

Estimasi pengeluaran bulanan
$0.00
Menghitung penghematan vs GPT-4o…

Kutip & sematkan

Badge Markdown GitHub
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BibTeX
@misc{llmpodium2026qwen38flashnext,
  title = {Benchmark Analysis and Intelligence Evaluation of Qwen3.8 Flash Next},
  author = {LLMPodium Research},
  year = {2026},
  url = {https://llmpodium.com/models/qwen3-8-flash-next}
}

Sematkan Lencana Skor

Tambahkan lencana Podium Score langsung ke README GitHub atau Hugging Face Anda — menautkan ke peringkat langsung.

Pratinjau
Qwen3.8 Flash Next LLMPodium Score
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