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AI Choice Engine

Model comparison

GPT-6 Sol vs Qwen3-235B-A22B

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

Catalog record checked September 26, 2026Individual provider fields may changeEvidence confidence: High — see receipts below

OpenAI

GPT-6 Sol

Balanced

vs

Qwen

Qwen3-235B-A22B

Balanced · Open weights

AI model capability comparison
SpecificationGPT-6 SolQwen3-235B-A22B
ProviderOpenAIQwen
TierBalancedBalanced
Context windowWinner: 1.05M128K
Max outputWinner: 128K33K
Input / 1M tokens$2Winner: $0.70
Output / 1M tokens$10Winner: $2.80
WeightsClosedOpen
ParametersGPT-6 mid-price tier235B total / 22B active (MoE)
Reasoning levelsnone, low, medium, high, xhigh, maxlow, high, max
Modalitiestext, imagetext
LicenseNot disclosedUnverifiedApache 2.0
API model idgpt-6-solqwen3-235b-a22b
ReleasedSeptember 22, 2026April 29, 2025
Artificial Analysis Intelligence Index [max] (2026-09-26)47.5Not verifiedUnverified
Artificial Analysis Intelligence Index (2026-09-26)Not verifiedUnverified9.5
DeepSWE 1.1 [max] (2026-09-22)68.8Not verifiedUnverified
OSWorld 2.0 (offline) [xhigh] (2026-09-22)60.5Not verifiedUnverified
Terminal-Bench 4.0 [max] (2026-09-26)43.9Not verifiedUnverified

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

Where each score comes from, and how far it can be compared across models.

  • 2026-09-26
    Artificial Analysis Terminal-Bench 4.0 (independent AA run, part of Intelligence Index v4.3.2)

    Agentic terminal work: long multi-step tasks executed in a sandboxed shell environment.

    Comparable with caveatNOT comparable with Terminal-Bench 2.x or 3.0 — 4.0 uses a new, non-overlapping task set. Within 4.0, scores from different harnesses (tbench.ai agent entries vs Artificial Analysis runs) are not interchangeable.

  • 2026-09-26
    Artificial Analysis (Reasoning, AA-estimated)

    Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.

    Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check measuredAt.

  • 2026-09-22
    OpenAI — Introducing GPT-6 Sol and Luna (vendor)

    Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.

    Directly comparable

Pricing tiers

GPT-6 Sol: Replaces GPT-5.6 Sol at 50% of its $4/$20 promo price. Cached input $0.20/MTok; cache writes $2.50 (1.25x). Above 272K input the whole request bills $4/$15 ($0.40 cached). Batch/Flex $1/$5; Fast mode $4/$20. Knowledge cutoff Apr 20 2026. Launched in the API, ChatGPT Work and Codex the same day.

Qwen3-235B-A22B: Model Studio International $0.70 input / $2.80 output per MTok (non-thinking); thinking-mode output $8.40. Open weights (Apache 2.0) for self-hosting; 32,768-token recommended output.

BalancedRecord checked September 26, 2026

GPT-6 Sol

GPT-6 Sol brings GPT-6 Astra's training advances to OpenAI's mid-priced tier — OpenAI claims roughly half GPT-5.6 Sol's factual-error rate at half its price, with a 1.05M-token context.

Best for

  • Default OpenAI production model
  • Agentic coding
  • Long-context knowledge work

Watch out

Brand-new (2026-09-22): the DeepSWE figure is vendor-reported and it is not yet on the independent boards; long prompts above 272K bill $4/$15 for the whole request.

BalancedOpen weightsRecord checked September 26, 2026

Qwen3-235B-A22B

Qwen3-235B-A22B is Alibaba's widely deployed open-weight MoE — 235B/22B, strong price-performance.

Best for

  • Open-weight deployments
  • Self-hosting
  • Multilingual

Watch out

Needs multi-GPU for the 235B total; hosted rates vary.

When the cheaper one wins

Qwen3-235B-A22B is cheaper on output at $2.80 per million tokens against $10 for GPT-6 Sol — about 3.6×. 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.
  • 1 shared named benchmark with differing scores — Measured on: Artificial Analysis Intelligence Index.
  • Verified within the last 90 days — Newest catalog check was 2 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? GPT-6 Sol · Qwen3-235B-A22B

Common questions

GPT-6 Sol vs Qwen3-235B-A22B

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

Is GPT-6 Sol or Qwen3-235B-A22B cheaper for input?
Qwen3-235B-A22B is cheaper at $0.70 per million input tokens, against $2 for GPT-6 Sol — roughly 2.9× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-6 Sol has tiered pricing: Replaces GPT-5.6 Sol at 50% of its $4/$20 promo price. Cached input $0.20/MTok; cache writes $2.50 (1.25x). Above 272K input the whole request bills $4/$15 ($0.40 cached). Batch/Flex $1/$5; Fast mode $4/$20. Knowledge cutoff Apr 20 2026. Launched in the API, ChatGPT Work and Codex the same day. Qwen3-235B-A22B has tiered pricing: Model Studio International $0.70 input / $2.80 output per MTok (non-thinking); thinking-mode output $8.40. Open weights (Apache 2.0) for self-hosting; 32,768-token recommended output.
Is GPT-6 Sol or Qwen3-235B-A22B cheaper for output?
Qwen3-235B-A22B is cheaper at $2.80 per million output tokens, against $10 for GPT-6 Sol — roughly 3.6× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-6 Sol has tiered pricing: Replaces GPT-5.6 Sol at 50% of its $4/$20 promo price. Cached input $0.20/MTok; cache writes $2.50 (1.25x). Above 272K input the whole request bills $4/$15 ($0.40 cached). Batch/Flex $1/$5; Fast mode $4/$20. Knowledge cutoff Apr 20 2026. Launched in the API, ChatGPT Work and Codex the same day. Qwen3-235B-A22B has tiered pricing: Model Studio International $0.70 input / $2.80 output per MTok (non-thinking); thinking-mode output $8.40. Open weights (Apache 2.0) for self-hosting; 32,768-token recommended output.
Which has the larger context window, GPT-6 Sol or Qwen3-235B-A22B?
GPT-6 Sol accepts 1.05M tokens against 128K for Qwen3-235B-A22B. This only matters if you routinely send very long documents or large codebases.
Do GPT-6 Sol and Qwen3-235B-A22B support the same reasoning levels?
GPT-6 Sol exposes none, low, medium, high, xhigh, max, while Qwen3-235B-A22B exposes low, high, max.
Should I use GPT-6 Sol or Qwen3-235B-A22B?
Both sit in the balanced tier, so the choice usually comes down to price and context rather than capability. GPT-6 Sol suits default openai production model; Qwen3-235B-A22B suits open-weight deployments.
Can I self-host GPT-6 Sol or Qwen3-235B-A22B?
Qwen3-235B-A22B publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. GPT-6 Sol is a closed model whose supported access paths are controlled by its provider.

Next step

Choosing between them

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

  • Input price: Qwen3-235B-A22B costs $0.70 per 1M tokens versus $2 for GPT-6 Sol — a 2.9x difference at the headline tier.
  • Context: GPT-6 Sol takes 1.05M against 128K for Qwen3-235B-A22B — only decisive if your prompts approach the smaller window.
  • Deployment: Qwen3-235B-A22B publishes weights you can self-host; the other is API-only.
Run the model pickerEstimate stack costAll model comparisons

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