Amazon Nova Premier vs GLM 5.1

Amazon · US  |  Z.ai · China · Updated June 2026

Quick verdict

Pick Amazon Nova Premier for 1m-token context with deep aws bedrock integration or amazon's most capable nova model, positioned as a 'teacher' for distilling smaller models. Pick GLM 5.1 for long-horizon autonomous agentic engineering (up to 8-hour runs) or state-of-the-art open-weight coding (topped swe-bench pro at launch). Choose GLM 5.1 if you need self-hosting or data privacy; Amazon Nova Premier if you want a managed API.

Amazon Nova Premier (Amazon, US) and GLM 5.1 (Z.ai, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Amazon Nova Premier is amazon's flagship 1M-context Nova model on AWS Bedrock - a useful ecosystem anchor and distillation teacher, but weak and pricey on independent intelligence. GLM 5.1 is an open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecAmazon Nova PremierGLM 5.1
ProviderAmazon (US) Z.ai (China)
ReleasedApril 30, 2025 April 7, 2026
Context window1M (~1,500 pages) 200K (~300 pages)
Price (in/out)$2.5/$12.5 per 1M tokens $1.4/$4.4 per 1M tokens
Open weight?No — API only Yes — self-hostable
Modalitiestext, image text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

1M-token context with deep AWS Bedrock integration

Amazon Nova Premier

Its 1M window holds about 5× more than GLM 5.1's 200K in a single prompt.

Amazon's most capable Nova model, positioned as a 'teacher' for distilling smaller models

Amazon Nova Premier

Amazon's flagship 1M-context Nova model on AWS Bedrock - a useful ecosystem anchor and distillation teacher, but weak and pricey on independent intelligence — and it carries the larger 1M context.

A natural fit for teams already building on AWS

Amazon Nova Premier

Amazon Nova Premier lists a natural fit for teams already building on AWS among its strengths; GLM 5.1 does not.

Long-horizon autonomous agentic engineering (up to 8-hour runs)

GLM 5.1

An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours — and it runs cheaper at $1.4/$4.4 per 1M tokens.

State-of-the-art open-weight coding (topped SWE-Bench Pro at launch)

GLM 5.1

Open weights make this possible at all — Amazon Nova Premier is API-only, so it cannot leave the vendor's servers.

Sustained tool use across thousands of calls

GLM 5.1

An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours — and its weights are open while Amazon Nova Premier is API-only.

Lowest cost at scale

GLM 5.1

At $1.4/$4.4 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Amazon Nova Premier

Its 1M window is about 5× larger than GLM 5.1's 200K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

GLM 5.1

At $1.4/$4.4 per 1M tokens it undercuts Amazon Nova Premier, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

Amazon Nova Premier

Larger 1M window fits more in one prompt.

A team with data-privacy or self-hosting needs

GLM 5.1

Open weights let you run it on your own hardware; Amazon Nova Premier is API-only.

Anyone whose priority is 1m-token context with deep aws bedrock integration

Amazon Nova Premier

It is specifically built for that.

Anyone whose priority is long-horizon autonomous agentic engineering (up to 8-hour runs)

GLM 5.1

That is its strongest area.

An enterprise with regional data-residency rules

Amazon Nova Premier or GLM 5.1

Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

Amazon Nova Premier: where it fits

Amazon's flagship 1M-context Nova model on AWS Bedrock - a useful ecosystem anchor and distillation teacher, but weak and pricey on independent intelligence. Released April 30, 2025 by Amazon, it is built for 1M-token context with deep AWS Bedrock integration, amazon's most capable Nova model, positioned as a 'teacher' for distilling smaller models, a natural fit for teams already building on AWS, and multimodal input for complex reasoning across text and images.

Its trade-offs are real: weak on independent intelligence - Artificial Analysis Intelligence Index of 13, below average for its tier, expensive for its score at $2.50/$12.50 per million tokens, a 2025 model - older than the 2026 frontier it competes against, and sources disagree on modalities (Amazon cites image input; some evaluations list text-only). At $2.5 in / $12.5 out per million tokens, it sits in the mid price band.

GLM 5.1: where it fits

An open-weight (MIT) Chinese coding model built for long-horizon agentic engineering, topping SWE-Bench Pro at launch while running autonomously for up to 8 hours. Released April 7, 2026 by Z.ai, it is built for long-horizon autonomous agentic engineering (up to 8-hour runs), state-of-the-art open-weight coding (topped SWE-Bench Pro at launch), sustained tool use across thousands of calls, and self-hostable under a permissive MIT license.

Its trade-offs: text-only, with no image, audio, or video input, and 754B-parameter MoE demands heavy GPU resources to self-host. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.

The bottom line for this matchup

The defining split here is open vs. closed. GLM 5.1 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Amazon Nova Premier gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both Amazon Nova Premier and GLM 5.1 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is Amazon Nova Premier or GLM 5.1 better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Amazon Nova Premier leans toward 1m-token context with deep aws bedrock integration while GLM 5.1 leans toward long-horizon autonomous agentic engineering (up to 8-hour runs), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Amazon Nova Premier or GLM 5.1?

GLM 5.1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Amazon Nova Premier is API-metered at $2.5/$12.5 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

Amazon Nova Premier — 1M vs 200K, about 5× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Amazon Nova Premier and GLM 5.1 together?

Yes — a multi-model platform like LumiChats gives you Amazon Nova Premier, GLM 5.1 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, Amazon Nova Premier or GLM 5.1?

GLM 5.1 — released April 7, 2026, about 11 months after Amazon Nova Premier.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.