GLM-5.3 is Z.ai's best model for long-context work — it scores 87/100 vs 86/100 for GLM-5.2, at $1.4/1M input tokens. Across all providers, Claude Fable 5 still leads long-context work at 99/100 — worth considering if you're not committed to Z.ai.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
GLM-5.3 is the safest overall answer here when you want the strongest default instead of the lowest list price.
Mistral: Mistral Nemo is the lower-cost option to start with when you still need useful output at scale.
GLM-5.2 is the better pick when large documents, transcripts, or knowledge-heavy work lead the decision.
GLM-5.3 leads Z.ai's lineup for long-context work at 87/100 ($1.4/1M input, 1M context).
GLM-5.3 Flash is the value pick at $0.15/1M input with a long-context work score of 86/100.
Claude Fable 5 (Anthropic) is the overall long-context work leader at 99/100 if provider choice is open.
Choose GLM-5.3 when long-context work quality is the priority and you're staying on Z.ai.
Choose GLM-5.3 Flash when token volume matters more than peak quality.
Teams open to other providers should also evaluate Claude Fable 5 before committing.
Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.
Z.ai / Budget / Aug 27, 2026
Native vision and video, MIT weights, fifteen cents per million.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need top-tier reasoning or a published SWE-bench Verified figure — this is a volume model, not a ceiling model.
The fastest way to see where the recommendation shifts when your priority changes.
Same price as GLM-5.2, far stronger on agents and security.
Top open-weights coder — beats GPT-5.5 at a sixth of the cost.
Native vision and video, MIT weights, fifteen cents per million.
Every figure below is the provider's list price or a published capability score — the same numbers the recommendation on this page is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GLM-5.3Z.ai | $1.40/1M | $4.40/1M | $23 | 1M tokens | Balanced | 91 | 79 | 82 |
| GLM-5.2Z.ai | $1.40/1M | $4.40/1M | $23 | 1M tokens | Balanced | 90 | 78 | 80 |
| GLM-5.3 FlashZ.ai | $0.15/1M | $0.50/1M | $2.50 | 1M tokens | Fast | 82 | 76 | 78 |
Capability scores are out of 100 and reflect our own weighting of published benchmarks and production signals — see how we evaluate models. “Est. month” assumes 10M input and 2M output tokens at list price, with no batch or caching discounts applied, so treat it as a ceiling.
What each one is genuinely good at, where it falls down, and the situations we would steer you away from it — not just the headline score.
Z.ai's newest flagship, aimed squarely at software engineering, autonomous agents and cybersecurity — and the first open-weights model to beat Claude Mythos 5 on a security benchmark.
Your procurement process requires a SWE-bench Verified figure, or you need the closed-frontier reasoning ceiling.
The clear upgrade over GLM-5.2 at the same price — take it unless you specifically need a published SWE-bench Verified number to sign off on. The security benchmark lead over Mythos 5 is the genuinely new thing here.
Released August 14, 2026. Z.ai list pricing is $1.40/$4.40, the same rate as GLM-5.2; resellers discount from that list. Also available through the GLM Coding Plan from $18/mo. Reported GPQA Diamond 91.7% and Artificial Analysis Intelligence Index 59.5.
Z.ai's MIT-licensed open-weight flagship — the top open-weights coding model of mid-2026, beating GPT-5.5 on agentic coding benchmarks at roughly a sixth of the cost.
You need frontier reasoning ceiling or launch-day verified benchmarks.
The open-weights coding value king of mid-2026 — GPT-5.5-beating agentic coding at a fraction of the price, with an MIT license. DeepSeek V4-Flash undercuts it on price; GLM-5.2 answers with higher ceiling and two reasoning-effort modes.
Announced June 13, 2026; pay-per-token API live June 16. Two reasoning modes ('thinking' and 'max thinking'). GLM Coding Plan: Lite $18/mo (~$12.60 effective yearly), Pro $72, Max $160.
A 320B-A18B mixture-of-experts model with native vision and video, MIT-licensed weights and a 1M context — at fifteen cents per million input tokens.
You need top-tier reasoning or a published SWE-bench Verified figure — this is a volume model, not a ceiling model.
The cheapest genuinely multimodal 1M-context model worth using in August 2026. If you are running vision or video at volume and can host weights, nothing at this price is close.
Released August 26, 2026. 320B total parameters, 18B active (320B-A18B MoE). Z.ai lists $0.15/1M input, $0.03/1M cached input and $0.50/1M output, with a launch promotion halving those rates through September 9, 2026. Self-reported against GLM-5.2: DeepSWE 63.4 vs 46.2, AutomationBench 48.8 vs 26.2.
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GLM-5.3 — it scores 87/100 on long-context work in this directory, ahead of GLM-5.2 at 86/100. Same price as GLM-5.2, far stronger on agents and security.
Not overall. Claude Fable 5 (Anthropic) leads the directory for long-context work at 99/100 vs GLM-5.3's 87/100. GLM-5.3 is the best pick if you're staying within Z.ai's ecosystem.
GLM-5.3 Flash at $0.15/1M input tokens (long-context work score: 86/100). Use it for volume work and reserve GLM-5.3 for the tasks where quality matters most.
$1.4/1M input tokens and $4.4/1M output tokens via the API, or through GLM Coding Lite at $18/mo for chat use. Context window: 1M tokens. On a moderate month — 10M input and 2M output tokens — that works out to about $22.80, against $2.50 for GLM-5.3 Flash.
No published SWE-bench Verified score, so it is absent from the benchmark most buyers compare on. Priced identically to GLM-5.2 at $1.40/$4.40 — the upgrade is capability, not value. Concretely, avoid it if your procurement process requires a SWE-bench Verified figure, or you need the closed-frontier reasoning ceiling. If none of that is negotiable, Claude Fable 5 (Anthropic) is the cross-provider leader at 99/100.
long-horizon autonomous engineering tasks where GLM-5.2 ran out of headroom, offensive and defensive security tooling — 84.5% on CyberGym, ahead of Claude Mythos 5, and self-hosted or coding-plan deployments that need frontier-adjacent quality at open-weights pricing. Its 1M-token context window is the practical limit on how much you can hand it in one go.
GLM-5.3 scores 87/100 on long-context work against 86/100 for GLM-5.3 Flash, at 9x the input price. That premium is worth it on work where a wrong answer costs real time or money, and hard to justify on high-volume, low-stakes calls. Most teams run both and route by task rather than picking one.