Model comparison

Gemini 3.8 Flash vs Qwen3-235B-A22B

Google 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

Google

Gemini 3.8 Flash

Balanced

Qwen

Qwen3-235B-A22B

Balanced · Open weights

AI model capability comparison
Specification	Gemini 3.8 Flash	Qwen3-235B-A22B
Provider
Provider	Google	Qwen
Tier
Tier	Balanced	Balanced
Context window
Context window	Winner: 1.05M	128K
Max output
Max output	Winner: 66K	33K
Input / 1M tokens
Input / 1M tokens	$0.75	Winner: $0.70
Output / 1M tokens
Output / 1M tokens	$3.75	Winner: $2.80
Weights
Weights	Closed	Open
Parameters
Parameters	Not disclosedUnverified	235B total / 22B active (MoE)
Reasoning levels
Reasoning levels	low, medium, high	low, high, max
Modalities
Modalities	text, image, video, audio, pdf	text
License
License	Not disclosedUnverified	Apache 2.0
API model id
API model id	gemini-3.8-flash	qwen3-235b-a22b
Released
Released	September 2, 2026	April 29, 2025
Artificial Analysis Intelligence Index [high] (2026-09-26)
Artificial Analysis Intelligence Index [high] (2026-09-26)	40.9	Not verifiedUnverified
Artificial Analysis Intelligence Index (2026-09-26)
Artificial Analysis Intelligence Index (2026-09-26)	Not verifiedUnverified	9.5
Terminal-Bench 2.1 (2026-09-02)
Terminal-Bench 2.1 (2026-09-02)	89.4	Not verifiedUnverified
Humanity's Last Exam (2026-09-02)
Humanity's Last Exam (2026-09-02)	54.9	Not 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 (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.
2026-09-02
Google official blog (vendor, HLE-Verified full set)
Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.
Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
2026-09-02
Google launch eval table (transcribed by Vellum)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

Pricing tiers

Gemini 3.8 Flash: Intro $0.75/$3.75 per MTok through 2026-12-31, rising to $1.50/$7.50 from 2027-01-01; batch/Flex half price. Free tier available. Companion Gemini 3.8 Flash Cyber (defensive security) is restricted to the Fairwind Program with no public pricing.

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

Gemini 3.8 Flash

Gemini 3.8 Flash is Google's most capable workhorse Flash — within a point of the top of DeepSWE 1.1 at a fraction of frontier cost per task.

Best for

Agentic coding at scale
Mid-difficulty engineering
Multimodal pipelines

Watch out

Intro pricing doubles on 2027-01-01, and Google says it can consume more tokens than 3.7 Flash — 3.7 Flash stays available for efficiency-first workloads.

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.

Benchmark

DeepSWE 1.1 in context

Only Gemini 3.8 Flash has a published DeepSWE 1.1 result. It is shown against the wider field, with cost per completed task alongside the score.

Local leader

GPT-6 Astra [xhigh]

74%

Rows shown

Best published Pass@1 per model (Datacurve's default view)

Snapshot date

2026-09-26

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	GPT-6 Astra [xhigh]	74%	$4.43	30k	29
2	Gemini 3.8 Flash [high]	74%	$2.36	143k	166
3	Claude Opus 5 [max]	74%	$11.84	118k	99
4	GPT-5.6 Sol [max]	73%	$8.39	60k	61
5	Claude Fable 5 [xhigh]	70%	$13.41	80k	68
6	GPT-5.6 Terra [max]	70%	$4.95	72k	76
7	GLM 5.3 [max]	69%	$3.99	80k	124
8	Kimi K3 [max]	69%	$4.65	82k	98
9	Grok 4.6 [medium]	68%	$3.45	50k	70
10	GPT-5.6 Luna [max]	67%	$3.03	73k	102
11	GPT-5.5 [xhigh]	67%	$7.23	46k	82
12	Gemini 3.7 Flash [medium]	66%	$2.03	94k	117
13	GLM 5.3 Flash [max]	63%	$0.48	73k	123
14	DeepSeek V4-Pro [max]	63%	$0.24	106k	155
15	Claude Opus 4.8 [max]	59%	$13.22	135k	120
16	Qwen3.8-Max [xhigh]	58%	$3.73	95k	111
17	Muse Spark 1.2 [xhigh]	55%	$3.70	99k	101
18	Claude Sonnet 5 [max]	54%	$26.40	214k	268
19	Grok 4.5 [high]	54%	$2.42	36k	61
20	DeepSeek V4-Flash [max]	53%	$0.10	108k	153
21	Muse Spark 1.1 [xhigh]	53%	$2.36	74k	96
22	GPT-5.4 [xhigh]	52%	$5.65	71k	70
23	Gemini 3.6 Flash [high]	47%	$4.42	96k	117
24	GLM 5.2 [max]	44%	$3.92	78k	129
25	Gemini 3.5 Flash [high]	36%	$3.45	76k	105
26	Kimi K2.7 Code	31%	$2.82	59k	149
27	Claude Sonnet 4.6 [high]	30%	$5.52	76k	134
28	Gemini 3.1 Pro [high]	12%	$2.14	28k	76

“Best per model” shows each model’s highest published Pass@1, as Datacurve’s own board does; switch to all effort levels to see every configuration. Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.

When the cheaper one wins

Qwen3-235B-A22B is cheaper on output at $2.80 per million tokens against $3.75 for Gemini 3.8 Flash — about 1.3×. 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.
4/5 core specs verified on both sides — Not published for at least one side: parameter count.
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.

Gemini 3.8 Flash: Google — Gemini 3.8 Flash and 3.8 Flash Cyber (accessed 2026-09-03)
Gemini 3.8 Flash: Gemini API pricing (gemini-3.8-flash $0.75/$3.75 intro) (accessed 2026-09-03)
Gemini 3.8 Flash: DeepSWE 1.1 leaderboard — gemini-3.8-flash [high] 73.8% at $2.36/task (accessed 2026-09-03)
Qwen3-235B-A22B: HuggingFace — Qwen3-235B-A22B (accessed 2026-08-29)
Qwen3-235B-A22B: Qwen — Qwen3 (accessed 2026-08-29)
Qwen3-235B-A22B: Alibaba Cloud Model Studio pricing (qwen3-235b-a22b $0.70/$2.80, thinking $8.40) (accessed 2026-09-26)

Related comparisons

Diving deeper on one model? Gemini 3.8 Flash · Qwen3-235B-A22B

Common questions

Gemini 3.8 Flash vs Qwen3-235B-A22B

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

Is Gemini 3.8 Flash or Qwen3-235B-A22B cheaper for input?

Qwen3-235B-A22B is cheaper at $0.70 per million input tokens, against $0.75 for Gemini 3.8 Flash — roughly 1.1× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.8 Flash has tiered pricing: Intro $0.75/$3.75 per MTok through 2026-12-31, rising to $1.50/$7.50 from 2027-01-01; batch/Flex half price. Free tier available. Companion Gemini 3.8 Flash Cyber (defensive security) is restricted to the Fairwind Program with no public pricing. 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 Gemini 3.8 Flash or Qwen3-235B-A22B cheaper for output?

Qwen3-235B-A22B is cheaper at $2.80 per million output tokens, against $3.75 for Gemini 3.8 Flash — roughly 1.3× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.8 Flash has tiered pricing: Intro $0.75/$3.75 per MTok through 2026-12-31, rising to $1.50/$7.50 from 2027-01-01; batch/Flex half price. Free tier available. Companion Gemini 3.8 Flash Cyber (defensive security) is restricted to the Fairwind Program with no public pricing. 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, Gemini 3.8 Flash or Qwen3-235B-A22B?

Gemini 3.8 Flash accepts 1.05M tokens against 128K for Qwen3-235B-A22B. This only matters if you routinely send very long documents or large codebases.

Do Gemini 3.8 Flash and Qwen3-235B-A22B support the same reasoning levels?

Gemini 3.8 Flash exposes low, medium, high, while Qwen3-235B-A22B exposes low, high, max.

Should I use Gemini 3.8 Flash or Qwen3-235B-A22B?

Both sit in the balanced tier, so the choice usually comes down to price and context rather than capability. Gemini 3.8 Flash suits agentic coding at scale; Qwen3-235B-A22B suits open-weight deployments.

Can I self-host Gemini 3.8 Flash 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. Gemini 3.8 Flash 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 $0.75 for Gemini 3.8 Flash — a 1.1x difference at the headline tier.
Context: Gemini 3.8 Flash 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 picker Estimate stack cost All model comparisons

Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.