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Model comparison

DeepSeek V4 Pro vs Qwen 3.8 Max

DeepSeek against Qwen, compared on context, price, and verified benchmark results.

Catalog record checked September 3, 2026Individual provider fields may changeEvidence confidence: High — see receipts below

DeepSeek

DeepSeek V4 Pro

Frontier · Open weights

vs

Qwen

Qwen 3.8 Max

Frontier · Open weights

AI model capability comparison
SpecificationDeepSeek V4 ProQwen 3.8 Max
ProviderDeepSeekQwen
TierFrontierFrontier
Context windowWinner: 1M991K
Max outputWinner: 384K131K
Input / 1M tokensWinner: $1.32$2
Output / 1M tokensWinner: $3.96$6
WeightsOpenOpen
Parameters1.6T total / 49B active (MoE)2.4T total / 95B active (MoE)
Reasoning levelslow, high, maxlow, high, max
Modalitiestexttext, image, video
LicenseMITNot disclosedUnverified
API model iddeepseek-v4-proqwen3.8-max
ReleasedApril 24, 2026August 3, 2026
Artificial Analysis Intelligence Index (2026-08-14)53Winner: 58
SWE-bench Verified (2026-04-24)80.6Winner: 85.6
GPQA Diamond (2026-04-24)90.1Winner: 92.6
Humanity's Last Exam (2026-04-24)48.2Winner: 56.2
MMLU-Pro (2026-04-24)87.5Not verifiedUnverified
Terminal-Bench 2.1 (2026-08-03)Not verifiedUnverified86.6

Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

  • 2026-09-01: vals.ai SWE-bench Verified leaderboard (independent, mini-swe-agent bash-only); not published by Qwen (official used SWE-bench Pro 67.7)Real GitHub issue resolution: does the model's patch pass the hidden tests. Comparability: comparable with caveat — Post-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
  • 2026-08-14: Z.ai GLM-5.3 blog (independent Z.ai-run, with tools; Qwen official no-tools 43.6 — both preserved)Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise. Comparability: comparable with caveat — Subset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
  • 2026-08-14: Artificial AnalysisComposite index blending reasoning, knowledge, and coding evals into one 0–100 score. Comparability: directly comparable — AA occasionally rebaselines the index scale between snapshots — a score captured on one date is only comparable to same-snapshot scores (check measuredAt).
  • 2026-08-03: Qwen official blog (vendor-run table)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
  • 2026-04-24: DeepSeek V4 Pro HF model card (vendor, Think Max, exact match)Real GitHub issue resolution: does the model's patch pass the hidden tests. Comparability: comparable with caveat — Post-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.

Pricing tiers: DeepSeek V4 Pro: Official peak rates $1.32/$3.96 per MTok; off-peak $0.66/$1.98 (cache-miss). Open weights (MIT). 1.6T/49B active MoE. · Qwen 3.8 Max: $2/$6 per MTok (Alibaba Cloud Model Studio). Weights announced open, verify they have dropped.

FrontierOpen weightsRecord checked September 3, 2026

DeepSeek V4 Pro

DeepSeek V4 Pro is the open-weight (MIT) flagship with a 1M-token context at a fraction of frontier API cost.

Best for

  • Cost-sensitive hosted agents
  • Open-weight deployments
  • High-volume coding

Watch out

Self-hosting needs datacentre VRAM; hosted rates vary by provider. Price single-source — verify.

FrontierOpen weightsRecord checked September 3, 2026

Qwen 3.8 Max

Qwen 3.8 Max is Alibaba's flagship — 2.4T MoE with 1M-class context, near frontier on the Intelligence Index.

Best for

  • Open-weight frontier work
  • Long-context
  • Multimodal

Watch out

Open weights dropped 2026-08-12 under a custom (non-Apache) licence with vision and 1M-context stripped from the open checkpoint — the open checkpoint is not the full API model. Verify the licence before commercial use.

When the cheaper one wins

DeepSeek V4 Pro is cheaper on output at $3.96 per million tokens against $6 for Qwen 3.8 Max — about 1.5×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.

Run the model picker

Evidence confidence: High

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

  • Pricing verified on both sides — Input and output rates are verified for both models.
  • 5/5 core specs verified on both sides — All core specifications verified for both models.
  • 4 shared named benchmarks with differing scores — Measured on: Artificial Analysis Intelligence Index, SWE-bench Verified, GPQA Diamond, Humanity's Last Exam.
  • Verified within the last 90 days — Newest catalog check was 8 days ago.
  • Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

Related comparisons

Diving deeper on one model? DeepSeek V4 Pro · Qwen 3.8 Max

Common questions

DeepSeek V4 Pro vs Qwen 3.8 Max

Answered from the verified figures on this page rather than general guidance.

Is DeepSeek V4 Pro or Qwen 3.8 Max cheaper for input?

DeepSeek V4 Pro is cheaper at $1.32 per million input tokens, against $2 for Qwen 3.8 Max — roughly 1.5× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; DeepSeek V4 Pro has tiered pricing: Official peak rates $1.32/$3.96 per MTok; off-peak $0.66/$1.98 (cache-miss). Open weights (MIT). 1.6T/49B active MoE. Qwen 3.8 Max has tiered pricing: $2/$6 per MTok (Alibaba Cloud Model Studio). Weights announced open, verify they have dropped.

Is DeepSeek V4 Pro or Qwen 3.8 Max cheaper for output?

DeepSeek V4 Pro is cheaper at $3.96 per million output tokens, against $6 for Qwen 3.8 Max — roughly 1.5× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; DeepSeek V4 Pro has tiered pricing: Official peak rates $1.32/$3.96 per MTok; off-peak $0.66/$1.98 (cache-miss). Open weights (MIT). 1.6T/49B active MoE. Qwen 3.8 Max has tiered pricing: $2/$6 per MTok (Alibaba Cloud Model Studio). Weights announced open, verify they have dropped.

Which has the larger context window, DeepSeek V4 Pro or Qwen 3.8 Max?

DeepSeek V4 Pro accepts 1M tokens against 991K for Qwen 3.8 Max. This only matters if you routinely send very long documents or large codebases.

Do DeepSeek V4 Pro and Qwen 3.8 Max support the same reasoning levels?

Yes — both accept the same effort settings: "low", "high", "max". Higher effort costs more and takes longer, so start low and raise it only where output quality actually improves.

Should I use DeepSeek V4 Pro or Qwen 3.8 Max?

Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. DeepSeek V4 Pro suits cost-sensitive hosted agents; Qwen 3.8 Max suits open-weight frontier work.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Input price: DeepSeek V4 Pro costs $1.32 per 1M tokens versus $2 for Qwen 3.8 Max — a 1.5x difference at the headline tier.
  • Context: DeepSeek V4 Pro takes 1M against 991K for Qwen 3.8 Max — only decisive if your prompts approach the smaller window.
  • Measured capability: Qwen 3.8 Max leads Artificial Analysis Intelligence Index 58 to 53 (measured 2026-08-14).
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