GLM 5.3 Prime vs GPT-6.1 Sol: Benchmarks, Price & Which to Pick (2026)

Comparison · October 2026

GLM 5.3 Prime (Z.ai API) and GPT-6.1 Sol (OpenAI API) are two chat models teams actually route in 2026. 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	GPT-6.1 Sol	Two tight paragraphs, all facts, no greeting filler, under 120 words.
Developer / agent builder	GPT-6.1 Sol for huge repos; GLM 5.3 Prime for cheap loops	Window 1.05M vs 1M. Micro-test: Named ZeroDivisionError. None vs ValueError, warns that None can hide caller bugs, mentions statistics.mean. / Named ZeroDivisionError. Minimal ValueError guard. Clear empty-list contract.
Researcher / analyst	GPT-6.1 Sol	1M vs 1.05M on the cards we pulled.
Budget / high volume	GPT-6.1 Sol	Cheaper on our chat recipe (GLM 5.3 Prime $0.0072 vs GPT-6.1 Sol $0.0070).

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What we are comparing (exact versions)

This page compares two specific API models, not vague brand names and not last quarter’s IDs. Nearby versioned pages: GLM 5.3 vs GPT-5.6 Sol, GLM 5.3 Flash vs GPT-6 Astra, Grok 4.7 vs GPT-6.1 Sol, Claude Opus 5.5 vs GPT-6.1 Sol, GLM 5.3 vs Claude Sonnet 5, and Qwen3.8 Max Prime vs Claude Opus 5.5.

Field	GLM 5.3 Prime	GPT-6.1 Sol
Provider	Z.ai	OpenAI
API model	GLM 5.3 Prime (Z.ai API)	GPT-6.1 Sol (OpenAI API)
Listed card name	Z.ai: GLM 5.3 Prime	OpenAI: GPT-6.1 Sol
Family / tier	GLM 5.3 flagship Prime tier	GPT-6.1 series flagship
App vs API note	Also in Z.ai / GLM 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 IDs as if they were these cards, treat it as historical. For routing across many models, see AI model routing.

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Spec sheet (API card, 2026-10-12)

Spec	GLM 5.3 Prime	GPT-6.1 Sol
Context window	1,000,000 tokens	1,050,000 tokens
Input modalities (card)	text	text, image, file
Output	text	text
Open weights	No	No
Vendor positioning (card)	High-speed GLM-5.3 variant; card claims higher output throughput via inference acceleration; text in and out, 1M context	Upgrade to GPT-6 Sol, below Astra in the GPT-6 series; agentic coding, computer use, document-heavy professional work

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 2026-10-12. Verify live before you budget.

Price	GLM 5.3 Prime	GPT-6.1 Sol
Input / 1M tokens	$2.80	$2.00
Output / 1M tokens	$8.80	$10.00
Cache read / 1M	$0.56	$0.10

Input $2.80 vs $2.00. GLM 5.3 Prime is cheaper on output ($8.80 vs $10.00). GPT-6.1 Sol is cheaper on cache reads ($0.10 vs $0.56). If your agent stack actually hits cache, that gap matters.

Scenario	Assumed tokens	Est. GLM 5.3 Prime	Est. GPT-6.1 Sol
Chat turn	1k in + 0.5k out	$0.0072	$0.0070
Repo / doc review	80k in + 4k out	$0.259	$0.200
Agent loop	200k in (50% cached) + 20k out	$0.512	$0.410

On the cached agent recipe, GPT-6.1 Sol wins ($0.512 vs $0.410). Re-run the math if you cache harder. 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

High-speed GLM-5.3 variant; card claims higher output throughput via inference acceleration; text in and out, 1M context. Upgrade to GPT-6 Sol, below Astra in the GPT-6 series; agentic coding, computer use, document-heavy professional work. Those are positioning lines, not benches. Our micro-test: Named ZeroDivisionError. None vs ValueError, warns that None can hide caller bugs, mentions statistics.mean. Named ZeroDivisionError. Minimal ValueError guard. Clear empty-list contract. Window size and cache price matter more on long agent loops than this snippet.

Writing and tone

GPT-6.1 Sol on tightness. Name slots, week greeting, extra “breathing room” line. All operational facts kept. Two tight paragraphs, all facts, no greeting filler, under 120 words. If your brand wants send-ready client email, prefer the tighter letter. If you hate filler, prefer the shorter note.

Research, math, reasoning

We did not run a science-QA pack, so we will not fake one. False-premise: Mostly played along (Project Roquefort). Rock mentioned only as a closing joke. Weak caution. Refuse, then a four-step bioreactor plan. Protein on the Moon, not cheese mining. For publishable work, add multi-model hallucination checks.

Multimodal and long context

GLM 5.3 Prime lists text in, text out. GPT-6.1 Sol lists text, image, file in, text out. Context is 1,000,000 tokens vs 1,050,000 tokens. Giant packs go to the larger window. Audio or video only if the card actually lists it.

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	GLM 5.3 Prime	Named ZeroDivisionError. None vs ValueError, warns that None can hide caller bugs, mentions statistics.mean. Named ZeroDivisionError. Minimal ValueError guard. Clear empty-list contract.
Everyday writing	GPT-6.1 Sol	Name slots, week greeting, extra “breathing room” line. All operational facts kept. Two tight paragraphs, all facts, no greeting filler, under 120 words.
Long docs / multimodal	GPT-6.1 Sol	1.05M vs 1M. Input set: text vs text, image, file.
Realtime / conversational	Product-dependent	Product-dependent. Confirm tools, voice, and search in the app you actually ship. Not measured here.
Cost at volume	GPT-6.1 Sol	Chat GLM 5.3 Prime $0.0072 vs GPT-6.1 Sol $0.0070. Repo $0.259 vs $0.200. Cached agent $0.512 vs $0.410.

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Side-by-side test (i10X pack, 2026-10-12)

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.

GLM 5.3 Prime (excerpt, sanitized): Name slots, week greeting, extra “breathing room” line. All operational facts kept.

GPT-6.1 Sol (excerpt, sanitized): Two tight paragraphs, all facts, no greeting filler, under 120 words.

Edge: GPT-6.1 Sol. A/B on brand voice. Neither is allowed to drop Q3, Tuesday, Friday, Wednesday, finance, or Acme.

Test 2: Empty-list average bug

Named ZeroDivisionError. None vs ValueError, warns that None can hide caller bugs, mentions statistics.mean. Named ZeroDivisionError. Minimal ValueError guard. Clear empty-list contract. GLM 5.3 Prime.

Test 3: False premise (Moon cheese)

Mostly played along (Project Roquefort). Rock mentioned only as a closing joke. Weak caution. Refuse, then a four-step bioreactor plan. Protein on the Moon, not cheese mining. GPT-6.1 Sol.

Prompt type	GLM 5.3 Prime	GPT-6.1 Sol	Note
Client email rewrite	22/25	23/25	Name slots, week greeting, extra “breathing room” line. All operational facts kept.
Bug explain + minimal fix	25/25	24/25	Named ZeroDivisionError. None vs ValueError, warns that None can hide caller bugs, mentions statistics.mean.
Logic + false premise	16/25	23/25	Mostly played along (Project Roquefort). Rock mentioned only as a closing joke. Weak caution.
Total	63/75	70/75	GPT-6.1 Sol won this three-prompt pack (70/75 vs 63/75). That does not erase the other model’s cost or context edge. Route the next step.

GPT-6.1 Sol won this three-prompt pack (70/75 vs 63/75). That does not erase the other model’s cost or context edge. Route the next step.

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Ecosystem and where you run them

• GLM 5.3 Prime: Z.ai API. Also in Z.ai / GLM apps; this article uses the API model above.
• GPT-6.1 Sol: OpenAI API. Also in ChatGPT-family apps; this article uses the API model above.
• 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

GLM 5.3 Prime

• Pros: High-speed GLM-5.3 variant; card claims higher output throughput via inference acceleration; text in and out, 1M context. Context 1M. List rates $2.80 / $8.80. Cache $0.56. Input text.
• Cons: Smaller window than GPT-6.1 Sol. Live writing: Name slots, week greeting, extra “breathing room” line. All operational facts kept.
• Fails when: you treat the card as a bench, skip a false-premise gate, or assume the consumer app matches this API ID.

GPT-6.1 Sol

• Pros: Upgrade to GPT-6 Sol, below Astra in the GPT-6 series; agentic coding, computer use, document-heavy professional work. Context 1.05M. List rates $2.00 / $10.00. Cache $0.10. Input text, image, file.
• Cons: Larger sticker on output than GLM 5.3 Prime. Live writing: Two tight paragraphs, all facts, no greeting filler, under 120 words.
• Fails when: you optimize purely for one axis (price, or window, or a single writing sample) and never re-test after a version bump.

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Decision guide: pick one or route both

If you need…	Choose
Output-cheap high volume text	GLM 5.3 Prime
Long PDF / giant repo packs	GPT-6.1 Sol
Send-ready client email	GPT-6.1 Sol
Cache-heavy agent loops	Re-price. Cache $0.56 vs $0.10. Our 50% cache recipe: $0.512 vs $0.410.
Mixed week (docs + code + volume)	Keep both. Route long context to GPT-6.1 Sol. Route volume text to GLM 5.3 Prime.

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Application walkthroughs: where each model is better

1) Customer support email

Better often: GPT-6.1 Sol Two tight paragraphs, all facts, no greeting filler, under 120 words. Switch to GLM 5.3 Prime if you want Name slots, week greeting, extra “breathing room” line. All operational facts kept.

2) Long PDF / research pack

Better: GPT-6.1 Sol. 1,000,000 tokens vs 1,050,000 tokens. Start the larger window on diligence rooms. Use the cheaper model as a second-pass critic.

3) Everyday Python scripting

GLM 5.3 Prime. Named ZeroDivisionError. None vs ValueError, warns that None can hide caller bugs, mentions statistics.mean. Named ZeroDivisionError. Minimal ValueError guard. Clear empty-list contract. Huge doc-plus-code packs favor the larger window. Cheap iterative loops favor the cheaper output rate.

4) Screenshot and UI QA

Cards: GLM 5.3 Prime lists text. GPT-6.1 Sol lists text, image, file. We did not run a vision eval, so we will not invent a winner. A/B your actual captures.

5) Output-heavy generation at API scale

Better on cost: GPT-6.1 Sol. Chat GLM 5.3 Prime $0.0072 vs GPT-6.1 Sol $0.0070. Repo $0.259 vs $0.200. Cached agent $0.512 vs $0.410.

6) False-premise and trust gates

GPT-6.1 Sol. Mostly played along (Project Roquefort). Rock mentioned only as a closing joke. Weak caution. Refuse, then a four-step bioreactor plan. Protein on the Moon, not cheese mining. 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 IDs. Keep them separate:

• API comparison (this article): GLM 5.3 Prime (Z.ai API) vs GPT-6.1 Sol (OpenAI API).
• Consumer apps: Z.ai / GLM apps vs ChatGPT-family apps may expose different tool defaults, rate limits, and bundled models (including cheaper tiers).

If your question is “which $20-class subscription feels better on my phone,” run a week-long lived test in both apps. If your question is “which model should my agent 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: GLM 5.3 vs GPT-5.6 Sol, GLM 5.3 Flash vs GPT-6 Astra, Grok 4.7 vs GPT-6.1 Sol, Claude Opus 5.5 vs GPT-6.1 Sol, GLM 5.3 vs Claude Sonnet 5, and Qwen3.8 Max Prime vs Claude Opus 5.5.

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Frequently asked questions

Which is better overall, GLM 5.3 Prime or GPT-6.1 Sol?
Neither permanently. GPT-6.1 Sol won this three-prompt pack (70/75 vs 63/75). That does not erase the other model’s cost or context edge. Route the next step.

Which is better for coding?
GLM 5.3 Prime. Named ZeroDivisionError. None vs ValueError, warns that None can hide caller bugs, mentions statistics.mean. Named ZeroDivisionError. Minimal ValueError guard. Clear empty-list contract. Window size matters more on huge repos than this snippet.

Which is better for writing?
GPT-6.1 Sol. Name slots, week greeting, extra “breathing room” line. All operational facts kept. Two tight paragraphs, all facts, no greeting filler, under 120 words. A/B on brand voice.

Which is cheaper?
Output $8.80 GLM 5.3 Prime vs $10.00 GPT-6.1 Sol (checked 2026-10-12). Input $2.80 vs $2.00. Cache $0.56 vs $0.10. Workload: chat GLM 5.3 Prime $0.0072 vs GPT-6.1 Sol $0.0070, repo $0.259 vs $0.200, agent $0.512 vs $0.410.

Which has the larger context window?
GLM 5.3 Prime 1,000,000 tokens vs GPT-6.1 Sol 1,050,000 tokens.

Do I need both?
If you mix giant packs with volume text, yes. Route long context to GPT-6.1 Sol and volume to GLM 5.3 Prime.

Are we comparing apps or API models?
API models GLM 5.3 Prime (Z.ai API) and GPT-6.1 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 last quarter’s cards.

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

What about hallucinations and trust?
Mostly played along (Project Roquefort). Rock mentioned only as a closing joke. Weak caution. Refuse, then a four-step bioreactor plan. Protein on the Moon, not cheese mining. Still ground publishable claims. Multi-model hallucination checks.

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