Model comparison
Gemini 3.7 Flash vs Qwen 3.8 Flash Next
Google against Qwen, compared on context, price, and verified benchmark results.
Catalog record checked September 5, 2026Individual provider fields may changeEvidence confidence: Insufficient data — see receipts below
Gemini 3.7 Flash
Budget
Qwen
Qwen 3.8 Flash Next
Budget · Open weights
| Specification | Gemini 3.7 Flash | Qwen 3.8 Flash Next |
|---|---|---|
| Provider | Qwen | |
| Tier | Budget | Budget |
| Context window | Winner: 1.05M | 262K |
| Max output | 66K | Not verifiedUnverified |
| Input / 1M tokens | $0.75 | Not verifiedUnverified |
| Output / 1M tokens | $3.75 | Not verifiedUnverified |
| Weights | Closed | Open |
| Parameters | Not disclosedUnverified | 125B total / 6B active (MoE) + 51B n-gram embedding table |
| Reasoning levels | low, medium, high | Not verifiedUnverified |
| Modalities | text, image, video, audio, pdf | text, image, video |
| License | Not disclosedUnverified | qwen-community-1.0 |
| API model id | gemini-3.7-flash | qwen3.8-flash |
| Released | August 13, 2026 | August 26, 2026 |
| Artificial Analysis Intelligence Index [high] (2026-08-14) | 56 | Not verifiedUnverified |
| Artificial Analysis Intelligence Index (2026-08-28) | Not verifiedUnverified | 56 |
| Terminal-Bench 2.1 (2026-08) | 85.8 | Not verifiedUnverified |
| DeepSWE 1.1 (2026-08) | 65.3 | Not verifiedUnverified |
| Humanity's Last Exam (2026-08) | 53.6 | 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
- 2026-08-28: 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: Google DeepMind model evaluation report (vendor, HLE-Verified full set)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.
Pricing tiers: Gemini 3.7 Flash: Intro price $0.75/$3.75 per MTok through 2026-12-31, rising to $1.50/$7.50 from 2027-01-01. Multimodal input. · Qwen 3.8 Flash Next: QwenCloud reportedly prices it at $0.16/$0.47 per MTok (single source as of 2026-08-29 — left unverified). Native 262K context, extensible to 1M.
Gemini 3.7 Flash
Gemini 3.7 Flash is Google's efficiency-first multimodal Flash (3.8 Flash of 2026-09-02 is newer and stronger; Google keeps 3.7 for cost-focused routes).
Best for
- High-volume multimodal
- Agent loops
- Cost-sensitive work
Watch out
Intro pricing doubles on 2027-01-01; lock rates if deploying long-term.
Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is Alibaba's experimental preview of the Qwen 4 architecture — very low active parameters plus an unusual n-gram embedding component for cheap long-context.
Best for
- Early testing of Qwen 4 architecture
- Cheap high-throughput work
- Multimodal input
Watch out
Experimental preview; qwen-community-1.0 license is more restrictive than MIT/Apache; benchmark claims are vendor-reported.
When the cheaper one wins
One of these rows is missing a verified output price, so this page will not name a cheaper winner. Run the model picker against the actual job instead of guessing.
Run the model pickerEvidence confidence: Insufficient data
How strong and complete the evidence behind this comparison is — not a prediction of which model is better. Hard limitations apply: missing pricing.
- Pricing incomplete — No verified token pricing for Qwen 3.8 Flash Next.
- 2/5 core specs verified on both sides — Not published for at least one side: max output, parameter count, reasoning levels.
- 1 shared named benchmark (scores match) — Measured on: Artificial Analysis Intelligence Index — scores are equivalent, so the benchmark does not separate the pair.
- Verified within the last 90 days — Newest catalog check was 6 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 3.7 Flash: Google — Gemini 3.7 Flash (accessed 2026-08-29)
- Gemini 3.7 Flash: Google Cloud Gemini pricing (accessed 2026-08-29)
- Qwen 3.8 Flash Next: The New Stack — Qwen3.8-Flash previews Qwen4 (accessed 2026-08-29)
- Qwen 3.8 Flash Next: Yotta Labs — Qwen 3.8-Flash-Next specs (accessed 2026-08-29)
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Diving deeper on one model? Gemini 3.7 Flash · Qwen 3.8 Flash Next
Common questions
Gemini 3.7 Flash vs Qwen 3.8 Flash Next
Answered from the verified figures on this page rather than general guidance.
Which has the larger context window, Gemini 3.7 Flash or Qwen 3.8 Flash Next?
Gemini 3.7 Flash accepts 1.05M tokens against 262K for Qwen 3.8 Flash Next. This only matters if you routinely send very long documents or large codebases.
Should I use Gemini 3.7 Flash or Qwen 3.8 Flash Next?
Both sit in the budget tier, so the choice usually comes down to price and context rather than capability. Gemini 3.7 Flash suits high-volume multimodal; Qwen 3.8 Flash Next suits early testing of qwen 4 architecture.
Can I self-host Gemini 3.7 Flash or Qwen 3.8 Flash Next?
Qwen 3.8 Flash Next publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Gemini 3.7 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.
- Context: Gemini 3.7 Flash takes 1.05M against 262K for Qwen 3.8 Flash Next — only decisive if your prompts approach the smaller window.
- Deployment: Qwen 3.8 Flash Next publishes weights you can self-host; the other is API-only.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.