DeepSeek V4 vs Qwen3-Coder-Next
A frontier open-weight family against a compact, self-hostable coding model — parameters, pricing, and benchmarks compared.
Updated August 28, 2026 · Independent comparison — DeepSeek V4, Qwen3-Coder-Next are separate products.
DeepSeek V4
Open-weight frontier family (Pro + Flash)
DeepSeek V4 is an open-weight frontier family released April 24, 2026 in two sizes — V4-Pro (1.6T total / 49B active) and V4-Flash (284B total / 13B active) — both Mixture-of-Experts with a 1M-token context (now the default across DeepSeek's services) and up to 384K output. DeepSeek calls V4 open-source SOTA in agentic coding, claiming it beats all current open models in math, STEM, and coding, though it doesn't publish standalone SWE-bench numbers in its announcement. Pricing is peak/off-peak: Pro runs $0.66/$1.98 off-peak (rising to $1.32/$3.96 at peak) and Flash $0.22/$0.66 off-peak, undercutting most closed frontier models. Weights ship on Hugging Face and ModelScope (ids deepseek-v4-pro, deepseek-v4-flash), reachable via OpenAI-, Anthropic-, and Responses-style endpoints.
Qwen3-Coder-Next
Compact 80B/3B open-weight coding-agent model
Qwen3-Coder-Next is a compact open-weight (Apache-2.0) coding model from Alibaba's Qwen team, released February 2026 and built on the Qwen3-Next hybrid-attention MoE architecture. It has 80B total parameters but activates only 3B per token, giving strong coding quality at a fraction of the inference cost, and runs a 262,144-token native context (YaRN-extendable toward 1M). It's a non-thinking model optimized for direct tool calling, long-horizon agentic tasks, and recovery from execution failures, and its model card reports 70.6 on SWE-bench Verified, 44.3 on SWE-bench Pro, and 36.2 on Terminal-Bench 2.0. It self-hosts in ~46GB (4-bit GGUF) or serves via vLLM/SGLang, and is hosted on DashScope from $0.30/$1.50 per million tokens.
Bottom line
A frontier open-weight family against a compact, self-hostable coding model — parameters, pricing, and benchmarks compared. Choose DeepSeek V4 if you want a top-tier open frontier model and can run (or pay for) a large 1.6T/49B-active MoE; choose Qwen3-Coder-Next if you want a small, efficient coder you can self-host in ~46GB and run locally.
Model & Weights
| Feature | DeepSeek V4 | Qwen3-Coder-Next |
|---|---|---|
| Developer | DeepSeek | Alibaba (Qwen) |
| Parameters | Pro 1.6T/49B active; Flash 284B/13B active (MoE) | 80B total / 3B active (MoE) |
| Open weights | Yes — HF + ModelScope (MIT per model card) | Yes — Apache-2.0 (HF) |
| Multimodal input | Text (experimental Flash-vision variant) | Text (code) |
| Variants | V4-Pro, V4-Flash | Base + Instruct |
Context & Output
| Feature | DeepSeek V4 | Qwen3-Coder-Next |
|---|---|---|
| Context window | 1M tokens (Pro & Flash) | 262,144 native (YaRN to 1M) |
| Max output | Up to 384K tokens | 65,536 (suggested) |
| Thinking mode | Reasoning (V4) | No (non-thinking only) |
| Tool calling | ✓ | Yes (direct tool calling) |
Pricing (per 1M tokens)
| Feature | DeepSeek V4 | Qwen3-Coder-Next |
|---|---|---|
| API input | $0.66-$1.32 (Pro, off-peak/peak) | $0.30-$0.80 (by context tier) |
| API output | $1.98-$3.96 (Pro, off-peak/peak) | $1.50-$4.00 (by context tier) |
| Budget tier | V4-Flash $0.22/$0.66 (off-peak) | Self-host weights (free) |
| Pricing model | Peak / off-peak by UTC window | Tiered by context length (DashScope) |
Coding Benchmarks
| Feature | DeepSeek V4 | Qwen3-Coder-Next |
|---|---|---|
| SWE-bench Verified | Not published (numeric) | 70.6 |
| SWE-bench Pro | Not published (numeric) | 44.3 |
| Terminal-Bench | Not published | 36.2 (v2.0) |
| Vendor positioning | Open-source SOTA in agentic coding (DeepSeek) | Coding agents / local dev |
Deployment & Access
| Feature | DeepSeek V4 | Qwen3-Coder-Next |
|---|---|---|
| Servers | vLLM / SGLang | vLLM 0.15+, SGLang 0.5.8+, llama.cpp |
| Self-host footprint | Large (~865GB Pro / ~160GB Flash) | Small (~46GB 4-bit GGUF) |
| Hosted access | DeepSeek API, ClinePass, OpenCode Go | DashScope (qwen3-coder-next), Coding Plan Pro |
| Released | April 24, 2026 | February 2026 |
The Verdict
Choose DeepSeek V4 if...
- ✓You want a top-tier open frontier model and can run (or pay for) a large 1.6T/49B-active MoE.
- ✓You need a full 1M-token context with big outputs (up to 384K) for coding and reasoning.
- ✓You want downloadable weights on Hugging Face and ModelScope with a very cheap Flash tier.
- ✓You can schedule heavy jobs into DeepSeek's off-peak window to cut the bill further.
- ✓You value general reasoning breadth alongside coding.
Choose Qwen3-Coder-Next if...
- ✓You want a small, efficient coder you can self-host in ~46GB and run locally.
- ✓You're building IDE agents, CLI tools, or private copilots where inference cost dominates.
- ✓You want direct, reliable tool calling without thinking-mode overhead.
- ✓You prefer Apache-2.0 and broad server support (vLLM, SGLang, llama.cpp).
- ✓You want published, verified coding scores on a model that runs on modest hardware.
Frequently Asked Questions
What is the difference between DeepSeek V4 and Qwen3-Coder-Next?
DeepSeek V4 is an open-weight frontier family released April 24, 2026 in two sizes — V4-Pro (1.6T total / 49B active) and V4-Flash (284B total / 13B active) — both Mixture-of-Experts with a 1M-token context (now the default across DeepSeek's services) and up to 384K output. Qwen3-Coder-Next is a compact open-weight (Apache-2.0) coding model from Alibaba's Qwen team, released February 2026 and built on the Qwen3-Next hybrid-attention MoE architecture.
How much do DeepSeek V4 and Qwen3-Coder-Next cost?
DeepSeek V4: $0.66-$1.32 (Pro, off-peak/peak). Qwen3-Coder-Next: $0.30-$0.80 (by context tier). See the pricing table above for full plan details.
Should I choose DeepSeek V4 or Qwen3-Coder-Next?
Choose DeepSeek V4 if you want a top-tier open frontier model and can run (or pay for) a large 1.6T/49B-active MoE. Choose Qwen3-Coder-Next if you want a small, efficient coder you can self-host in ~46GB and run locally.
1DevToolSelf-host or hosted — wire either into your agent
DeepSeek V4 and Qwen3-Coder-Next are both open-weight and OpenAI-compatible, whether you self-host or hit a hosted endpoint. 1DevTool runs Claude Code, Codex CLI, Gemini CLI, Cline, and OpenCode side by side in persistent terminals — point them at a local vLLM/SGLang server or a DashScope key, switch per task, and use the built-in HTTP client, 13-engine database client, and embedded browser. One-time $29, no subscription.