Model comparison

GPT-5.6 Sol vs Qwen3.8-Max

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

Catalog record checked August 13, 2026; individual provider fields may change.

OpenAI

GPT-5.6 Sol

Frontier

Qwen

Qwen3.8-Max

Frontier

AI model capability comparison
Specification	GPT-5.6 Sol	Qwen3.8-Max
Provider	OpenAI	Qwen
Tier	Frontier	Frontier
Context window	Winner: 1.05M	1M
Max output	128K	Winner: 131K
Input / 1M tokens	$5	Winner: $2
Output / 1M tokens	$30	Winner: $6
Weights	Closed	Closed
Parameters	Not disclosed	2.4T total / ~95B active (MoE)
Reasoning levels	none, low, medium, high, xhigh, max	Not verified
Modalities	text, image	text, image, video
Released	July 9, 2026	August 3, 2026
Artificial Analysis Intelligence Index [max] (2026-08-08)	Winner: 61	58

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.

Pricing tiers: GPT-5.6 Sol: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.

Frontier

GPT-5.6 Sol

OpenAI's flagship tier, aimed at the hardest reasoning and agentic work.

Best for

Complex reasoning
Agentic workflows
Hard coding tasks

Watch out

25x Luna's input price — overspecified for routine generation. Sol Fast mode (where offered) is roughly ~2× price for ~2.5× speed — only enable when latency is the constraint. `gpt-5.2-chat-latest` / `gpt-5.3-chat-latest` shut down 2026-08-10 — migrate API chat traffic to Sol. ChatGPT Plus/Pro Chat got an Aug 6 Sol refresh + effort slider; Work/Codex/API July builds are unchanged.

Frontier

Qwen3.8-Max

Alibaba's hosted Max-class API — 2.4T MoE with a 1M context window (max input 991,808), vision, and managed tools. Distinct from the Hugging Face Qwen3.8-2.4T-A95B checkpoint.

Best for

Agentic coding
Multimodal knowledge work
Long-context delivery

Watch out

This row is the hosted Qwen3.8-Max API, not the downloadable checkpoint. The open-weight Qwen3.8-2.4T-A95B card is catalogued separately and does not include the API's vision, non-thinking, or built-in tool extras.

Benchmark

DeepSWE 1.1 in context

Both models shown against the wider field, with cost per completed task alongside the score.

Local leader

Claude Opus 5 [max]

74%

Rows shown

Highest published reasoning effort per model (not best Pass@1)

Snapshot date

2026-08-13

Mirrored from deepswe.datacurve.ai

Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.

DeepSWE 1.1 leaderboard with pass rate, cost, tokens, and steps per task
#	Model	Pass@1	Cost / task	Tokens / task	Steps / task
1	Claude Opus 5 [max]	74%	$11.84	118k	99
2	GPT-5.6 Sol [max]	73%	$8.39	60k	61
3	Claude Fable 5 [max]	70%	$21.63	119k	88
4	GPT-5.6 Terra [max]	70%	$4.95	72k	76
5	Kimi K3 [max]	69%	$4.65	82k	98
6	GPT-5.6 Luna [max]	67%	$3.03	73k	102
7	GPT-5.5 [xhigh]	67%	$7.23	46k	82
8	Grok 4.6 [xhigh]	67%	$5.50	71k	87
9	Gemini 3.7 Flash [high]	65%	$2.18	107k	125
10	DeepSeek V4-Pro [max]	63%	$0.24	106k	155
11	Claude Opus 4.8 [max]	59%	$13.22	135k	120
12	Qwen3.8-Max [xhigh]	58%	$3.73	95k	111
13	Muse Spark 1.2 [xhigh]	55%	$3.70	99k	101
14	Claude Sonnet 5 [max]	54%	$26.40	214k	268
15	Grok 4.5 [high]	54%	$2.42	36k	61
16	DeepSeek V4-Flash [max]	53%	$0.10	108k	153
17	Muse Spark 1.1 [xhigh]	53%	$2.36	74k	96
18	GPT-5.4 [xhigh]	52%	$5.65	71k	70
19	Gemini 3.6 Flash [high]	47%	$4.42	96k	117
20	GLM 5.2 [max]	44%	$3.92	78k	129
21	Gemini 3.5 Flash [high]	36%	$3.45	76k	105
22	Kimi K2.7 Code	31%	$2.82	59k	149
23	Claude Sonnet 4.6 [high]	30%	$5.52	76k	134
24	Gemini 3.1 Pro [high]	12%	$2.14	28k	76

DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.

Common questions

GPT-5.6 Sol vs Qwen3.8-Max

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

Is GPT-5.6 Sol or Qwen3.8-Max cheaper for input?

Qwen3.8-Max is cheaper at $2 per million input tokens, against $5 for GPT-5.6 Sol — roughly 2.5× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.6 Sol has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.

Is GPT-5.6 Sol or Qwen3.8-Max cheaper for output?

Qwen3.8-Max is cheaper at $6 per million output tokens, against $30 for GPT-5.6 Sol — roughly 5.0× 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-5.6 Sol has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.

Which has the larger context window, GPT-5.6 Sol or Qwen3.8-Max?

GPT-5.6 Sol accepts 1.05M tokens against 1M for Qwen3.8-Max. This only matters if you routinely send very long documents or large codebases.

Should I use GPT-5.6 Sol or Qwen3.8-Max?

Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GPT-5.6 Sol suits complex reasoning; Qwen3.8-Max suits agentic coding.

Next step

Choosing between them

Tier and workload decide this more reliably than a leaderboard position does.

If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.

If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.

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