Gemini 3.8 Flash vs GPT-5.6 Sol: Benchmarks, Price & Which to Pick (2026)

Comparison · September 2026

Gemini 3.8 Flash (Google API) and GPT-5.6 Sol (OpenAI API) are two models teams actually route in 2026: Google Flash price and media breadth versus OpenAI GPT-5.6 Sol flagship positioning. This is a decision guide, not a leaderboard dump: published API rates, three workload cost scenarios, live writing and coding snippets, and a pick matrix you can rerun. If you want both without juggling tabs, use a multi-model AI workspace or start on i10X.

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Persona picker

You are…	Start with	Why
Writer / marketer	Sol for send-ready short letters; Flash for warmer drafts	Both 23/25. Sol was tighter and shorter. Flash kept week-greeting energy.
Developer / agent builder	Either on the bug; Sol for flagship coding positioning	Both 24/25. Flash returned 0. Sol preferred ValueError for empty input. Sol’s card stresses multi-step coding.
Researcher / analyst	Flash when audio/video arrives; else either	Context nearly tied (~1.05M). Flash lists video and audio. Sol lists file, image, text.
Budget / high volume	Gemini 3.8 Flash	Chat $0.0026 vs $0.007. Agent $0.1575 vs $0.42.

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What we are comparing (exact versions)

This page compares two specific API models, not vague brand names. Multi-model AI means using more than one LLM in your stack and routing by job. For the method, see side-by-side AI comparison.

Field	Gemini 3.8 Flash	GPT-5.6 Sol
Provider	Google	OpenAI
API model	Gemini 3.8 Flash (Google API)	GPT-5.6 Sol (OpenAI API)
Listed card name	Google: Gemini 3.8 Flash	OpenAI: GPT-5.6 Sol
Family / tier	Gemini Flash (fast / volume)	GPT-5.6 series flagship
App vs API note	Also in Gemini apps; this article uses the API model above	Also in ChatGPT-family apps; this article uses the API model above

If a page still compares older version strings as if they were these API models, treat it as historical. For routing across many models, see AI model routing.

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Spec sheet (API card, 2026-09-07)

Spec	Gemini 3.8 Flash	GPT-5.6 Sol
Context window	1,048,576 tokens	1,050,000 tokens
Input modalities (card)	text, image, video, file, audio	file, image, text
Output	text	text
Open weights	No	No
Vendor positioning (card)	Gemini 3.8 Flash is Google’s most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.	GPT-5.6 Sol is the flagship model in OpenAI’s GPT-5.6 series. It is suited for complex reasoning, coding, and agentic workflows, and is particularly strong at command-line and multi-step coding tasks.

We did not invent max-output, reasoning-mode, or realtime-search rows. Those fields were not on the cards we pulled for this pair. If your product depends on a specific tool or effort switch, confirm it on the live vendor page before you ship.

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Pricing and real workload cost

List prices are easy to game. Workload cost is what you feel. Numbers below use published per-million rates as of 2026-09-07. Verify live before you budget.

Price	Gemini 3.8 Flash	GPT-5.6 Sol
Input / 1M tokens	$0.75	$2.00
Output / 1M tokens	$3.75	$10.00
Cache read / 1M	$0.075	$0.2

Gemini 3.8 Flash lists $0.75 input and $3.75 output. GPT-5.6 Sol lists $2.00 input and $10.00 output. Cache reads are $0.075 vs $0.2. If your agent stack actually hits cache, that gap matters. If it does not, output price dominates.

Scenario	Assumed tokens	Est. Gemini 3.8 Flash	Est. GPT-5.6 Sol
Chat turn	1k in + 0.5k out	$0.0026	$0.007
Repo / doc review	80k in + 4k out	$0.075	$0.2
Agent loop	200k in (50% cached) + 20k out	$0.1575	$0.42

Gemini 3.8 Flash wins every stylized workload we priced on list rates for this pair. Re-run the math if you cache harder or emit less. For subscription stacks, see AI subscription stack cost.

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Performance by job (not one score)

Public benches disagree by harness, effort mode, and date. We are not inventing index numbers we do not have for this pair. Treat vendor cards as positioning, treat our snippets as a small live pack, and prefer your own side-by-side on your prompts. Method: side-by-side AI comparison.

Coding and agents

Split. Micro-test tie. Sol: flagship command-line/agent positioning. Flash: cheaper loops and sum() shortcut. Card lines are positioning, not benches. Our empty-list micro-test is a narrow slice. For huge repos, context and cache pricing matter more than this snippet. For volume loops, list rates matter more than flagship branding.

Writing and tone

Sol for terse send-ready email. Flash for warmer greeting-led drafts. Same score (23/23). A writing win does not erase a cost win. Keep a second model for critique when the letter is customer-facing.

Research, math, reasoning

Nearly equal context. Flash wins when the pack includes audio or video. Sol when you want OpenAI flagship defaults. We did not run a science-QA pack, so we will not fake one. False-premise behavior is below. For publishable work, add multi-model hallucination checks.

Multimodal and long context

Gemini 3.8 Flash for modality count (adds video and audio). Sol matches text/image/file. Context is a near tie. Confirm the live card before you ship a media pipeline on an assumption from an older SKU.

Speed

No tok/s in this pack. Measure p50 from your API region. Do not ship on a third-party screenshot.

Job	Edge	Why
Hard coding / agents	Split	Split. Micro-test tie. Sol: flagship command-line/agent positioning. Flash: cheaper loops and sum() shortcut.
Everyday writing	Split	Sol for terse send-ready email. Flash for warmer greeting-led drafts. Same score (23/23).
Long docs / multimodal	Gemini 3.8 Flash	Gemini 3.8 Flash for modality count (adds video and audio). Sol matches text/image/file. Context is a near tie.
Realtime / conversational	Product-dependent	Product tooling and latency are app-specific. Not measured here.
Cost at volume	Gemini 3.8 Flash	Gemini 3.8 Flash. Chat $0.0026 vs $0.007. Repo $0.075 vs $0.2. Agent $0.1575 vs $0.42.

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Side-by-side test (i10X pack, 2026-09-07)

Same three prompts, scored 1-5 on instruction following, depth, factual caution, style, and usefulness (max 25). Writing, coding, and false premise only. No invented extra scores.

Test 1: Client email rewrite

Task: Keep every fact. Warmer. Under 120 words.

Gemini 3.8 Flash (excerpt, sanitized): warmer and longer; name slot; week greeting; Q3 deck last Tuesday; Finance still missing after Friday; ask to move stakeholders to next week, perhaps Wednesday; competitive slide needs new Acme pricing; short thanks close.

GPT-5.6 Sol (excerpt, sanitized): tight and short; name slot; Q3 deck last Tuesday; Finance still missing after Friday; ask to move stakeholders to next week, perhaps Wednesday; competitive slide needs new Acme pricing; short thanks close.

Edge: Flash: warm week greeting, Wednesday ask, Acme line. Sol: shorter, less greeting, same facts. Edge is tone, not completeness.

Test 2: Empty-list average bug

Gemini 3.8 Flash: names ZeroDivisionError; guards empty input; offers return 0; suggests sum() shortcut.

GPT-5.6 Sol: names ZeroDivisionError; guards empty input; prefers raising ValueError.

Both named ZeroDivisionError. Flash offered return 0 plus sum(). Sol preferred raising ValueError as clearer API. Tie on correctness.

Test 3: False premise (Moon cheese)

Gemini 3.8 Flash: refuses first (Moon is rock/regolith, not dairy); then redirects to ice, bioreactors, or habitat protein.

GPT-5.6 Sol: refuses first (Moon is rock/regolith, not dairy); then redirects to ice, bioreactors, or habitat protein.

Both refused the green-cheese Moon and redirected to rock, ice, or habitat protein. Both pass.

Prompt type	Gemini 3.8 Flash	GPT-5.6 Sol	Note
Client email rewrite	23/25	23/25	Flash: warm week greeting, Wednesday ask, Acme line. Sol: shorter, less greeting, same facts. Edge is tone, not completeness.
Bug explain + minimal fix	24/25	24/25	Both named ZeroDivisionError. Flash offered return 0 plus sum(). Sol preferred raising ValueError as clearer API. Tie on correctness.
Logic + false premise	22/25	22/25	See false-premise notes above.
Total	69/75	69/75	Pack total tied at 69/75. Jobs still split on cost, modalities, and tone.

Pack total tied at 69/75. Jobs still split on cost, modalities, and tone. Route the next step.

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Ecosystem and where you run them

• Gemini 3.8 Flash: Google API. Gemini consumer apps may bundle Flash differently. This page is Gemini 3.8 Flash (Google API).
• GPT-5.6 Sol: OpenAI API. ChatGPT apps may wrap Sol with different tools. This page is GPT-5.6 Sol (OpenAI API), not GPT-5.5 and not Sol Pro.
• Both in one place: Multi-model workspaces (including i10X) let you switch or compare without two native subscriptions for every test.

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Pros, cons, and failure modes

Gemini 3.8 Flash

• Pros: Cheaper on every workload. Audio/video listed. Matched pack total. Strong false-premise refuse.
• Cons: Flash positioning. Warmer tone may need edits. Not the OpenAI ecosystem.
• Fails when: you need OpenAI flagship branding for a customer-facing agent, or you hate greeting-heavy drafts.

GPT-5.6 Sol

• Pros: GPT-5.6 flagship card for complex reasoning and multi-step coding. Tighter short email. File input listed. Matched pack total.
• Cons: $2/$10 vs Flash $0.75/$3.75. No audio/video on this card. Higher agent-loop burn in our recipe.
• Fails when: you optimize for Flash-tier burn, or you need native audio/video input on this card.

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Decision guide: pick one or route both

If you need…	Choose
Lowest API burn at volume	Gemini 3.8 Flash
Long PDF / giant repo packs	Either (1.05M vs 1.05M)
Send-ready client email	A/B both. Scores were close; tone differs.
Broader multimodal inputs	Gemini 3.8 Flash
Mixed week (docs + code + volume)	Keep both. Route flagship work to the higher-priced card when needed. Route volume to Gemini 3.8 Flash.

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Application walkthroughs: where each model is better

1) Customer support email

Better often: GPT-5.6 Sol when the letter must go out short. Sol was tighter. Flash was warmer with greeting energy. Switch to Flash for volume or when media sits in the same thread.

2) Long PDF / research pack

Context: 1.05M vs 1.05M. Nearly equal context. Flash wins when the pack includes audio or video. Sol when you want OpenAI flagship defaults. Use the cheaper model as a second-pass critic when the first pass is flagship-priced.

3) Everyday Python scripting

Tie on the micro-test. Both caught the empty-list crash. Cheap iterative loops favor Gemini 3.8 Flash. Flagship positioning favors the higher-priced card when the product is hard engineering.

4) Screenshot and UI QA

Modality cards differ. Prefer the model that lists the media you actually send (Gemini 3.8 Flash: text, image, video, file, audio; GPT-5.6 Sol: file, image, text). We did not run a vision eval, so we will not invent a winner.

5) Output-heavy generation at API scale

Better on cost: Gemini 3.8 Flash. Chat $0.0026 vs $0.007. Repo $0.075 vs $0.2. Cached agent $0.1575 vs $0.42.

6) False-premise and trust gates

Both pass. Both refused the green-cheese Moon and redirected to rock, ice, or habitat protein. Both pass. For publishable claims, run a second model and a source check.

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Consumer plans vs API (do not mix them up)

SERP pages often blur consumer subscriptions with API model names. Keep them separate:

• API comparison (this article): Gemini 3.8 Flash (Google API) vs GPT-5.6 Sol (OpenAI API).
• Consumer apps: vendor chat apps may expose different tool defaults, rate limits, and bundled models (including cheaper tiers).

If your question is which subscription feels better on your phone, run a week-long lived test in both apps. If your question is which model your agent should call, use this API page.

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Speed notes

We did not measure tokens per second for this pair. Writeups in 2026 often disagree by region, batch size, and reasoning settings. For UX, log your own p50 and p95 from the API in the region you actually serve. Time-to-first-token and tokens per second are different feelings. Do not mix them.

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Related comparisons

Nearby pages: Claude Opus 5 vs Gemini 3.8 Flash, DeepSeek V4.1 Flash vs GPT-6 Astra, Claude Opus 5 vs DeepSeek V4.1 Flash, Gemini 3.8 Flash vs GPT-6 Astra, Grok 4.6 vs GPT-6 Astra, Claude Opus 5 vs GPT-6 Astra.

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Frequently asked questions

Which is better overall, Gemini 3.8 Flash or GPT-5.6 Sol?
Neither permanently. Pack total tied at 69/75. Cost and modalities still split the week. Pick by job.

Which is better for coding?
Micro-test was a tie on correctness (24/25 vs 24/25). Flagship cards differ. Volume loops favor Gemini 3.8 Flash.

Which is better for writing?
Sol for terse send-ready email. Flash for warmer greeting-led drafts. Same score (23/23). A/B on brand voice.

Which is cheaper?
On 2026-09-07 list rates, Gemini 3.8 Flash wins our three recipes (chat $0.0026 vs $0.007, repo $0.075 vs $0.2, agent $0.1575 vs $0.42). Verify live.

Which has the larger context window?
Gemini 3.8 Flash: 1,048,576 tokens. GPT-5.6 Sol: 1,050,000 tokens.

Do I need both?
If you mix flagship-hard jobs with volume text or mixed media, yes. Route expensive steps carefully and keep a cheap model for bulk.

Are we comparing apps or API models?
API models Gemini 3.8 Flash (Google API) and GPT-5.6 Sol (OpenAI API). Apps wrap different defaults.

How often should I re-test?
After version bumps. Monthly in production. Re-price when list rates move. Do not mix these IDs with older generation names.

Where can I run them side by side?
i10X. Method: side-by-side AI comparison.

What about hallucinations and trust?
See the false-premise scores above. Still ground publishable claims. Multi-model hallucination checks.

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