GPT-6.1 Terra vs Qwen3.8-Flash-Next
OpenAI · US | Alibaba · China · Updated June 2026
Quick verdict
Pick GPT-6.1 Terra for $1/$5 per million tokens (cached input $0.05) - half the price of gpt-6.1 sol and gpt-5.6 terra or terminal-bench 4.0 of 50.3% at maximum reasoning effort, versus 21.5% for gpt-5.6 terra (openai-reported). Pick Qwen3.8-Flash-Next for swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4) or cost efficiency: ~1/9th the training cost of qwen3.7-plus, ~12x cheaper api than flagship qwen3.8-max. Choose Qwen3.8-Flash-Next if you need self-hosting or data privacy; GPT-6.1 Terra if you want a managed API.
GPT-6.1 Terra (OpenAI, US) and Qwen3.8-Flash-Next (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GPT-6.1 Terra is openAI's October 26, 2026 mid-tier model - a 1.05M-context, computer-use-capable GPT at $1/$5 per million tokens, half the price of its predecessor. Qwen3.8-Flash-Next is alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max. 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
- ▸Price: Qwen3.8-Flash-Next is about 6.3× cheaper on input ($0.16/$0.47 per 1M tokens vs $1/$5 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
- ▸Context window: GPT-6.1 Terra holds 4× more — 1.05M tokens (~1,575 pages) vs 262K tokens natively (extensible to 1M with YaRN) (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: GPT-6.1 Terra is the newer model by about 2 months (released October 26, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | GPT-6.1 Terra | Qwen3.8-Flash-Next |
|---|---|---|
| Provider | OpenAI (US) | Alibaba (China) |
| Released | October 26, 2026 | August 26, 2026 |
| Context window | 1.05M tokens (~1,575 pages) | 262K tokens natively (extensible to 1M with YaRN) (~393 pages) |
| Price (in/out) | $1/$5 per 1M tokens | $0.16/$0.47 per 1M tokens |
| Open weight? | No — API only | Yes — self-hostable |
| Modalities | text, image, code | text, image, video |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
$1/$5 per million tokens (cached input $0.05) - half the price of GPT-6.1 Sol and GPT-5.6 Terra
GPT-6.1 Terra
Its 1.05M tokens window holds about 4× more than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN) in a single prompt.
Terminal-Bench 4.0 of 50.3% at maximum reasoning effort, versus 21.5% for GPT-5.6 Terra (OpenAI-reported)
GPT-6.1 Terra
OpenAI's October 26, 2026 mid-tier model - a 1.05M-context, computer-use-capable GPT at $1/$5 per million tokens, half the price of its predecessor — and it carries the larger 1.05M tokens context.
1.05M-token context with up to 128K output tokens, plus image input and computer use via the Responses API
GPT-6.1 Terra
OpenAI's October 26, 2026 mid-tier model - a 1.05M-context, computer-use-capable GPT at $1/$5 per million tokens, half the price of its predecessor — and it is the newer of the two.
SWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4)
Qwen3.8-Flash-Next
GPT-6.1 Terra is comparatively weak here — benchmark figures are vendor-reported; no SWE-bench Verified or MRCR numbers published
Cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max
Qwen3.8-Flash-Next
At $0.16/$0.47 per 1M tokens it undercuts GPT-6.1 Terra ($1/$5 per 1M tokens), and that gap compounds at volume.
Vision-based agentic tasks (AndroidWorld: 84.5)
Qwen3.8-Flash-Next
Alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max — and it runs cheaper at $0.16/$0.47 per 1M tokens.
Lowest cost at scale
Qwen3.8-Flash-Next
At $0.16/$0.47 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GPT-6.1 Terra
Its 1.05M tokens window is about 4× larger than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN), fitting roughly 1,575 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Qwen3.8-Flash-Next
At $0.16/$0.47 per 1M tokens it undercuts GPT-6.1 Terra, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-6.1 Terra
Larger 1.05M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Qwen3.8-Flash-Next
Open weights let you run it on your own hardware; GPT-6.1 Terra is API-only.
Anyone whose priority is $1/$5 per million tokens (cached input $0.05) - half the price of gpt-6.1 sol and gpt-5.6 terra
→ GPT-6.1 Terra
It is specifically built for that.
Anyone whose priority is swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4)
→ Qwen3.8-Flash-Next
That is its strongest area.
An enterprise with regional data-residency rules
→ GPT-6.1 Terra or Qwen3.8-Flash-Next
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GPT-6.1 Terra: where it fits
OpenAI's October 26, 2026 mid-tier model - a 1.05M-context, computer-use-capable GPT at $1/$5 per million tokens, half the price of its predecessor. Released October 26, 2026 by OpenAI, it is built for $1/$5 per million tokens (cached input $0.05) - half the price of GPT-6.1 Sol and GPT-5.6 Terra, terminal-Bench 4.0 of 50.3% at maximum reasoning effort, versus 21.5% for GPT-5.6 Terra (OpenAI-reported), 1.05M-token context with up to 128K output tokens, plus image input and computer use via the Responses API, and error rate on OpenAI's hard-prompt set falls from 53.6% (GPT-5.6 Terra) to 16.7% at low effort and 4.3% at max effort.
Its trade-offs are real: openAI still positions Sol and Astra as the higher-capability choices for demanding coding problems, requests above 272K input tokens are billed at 2x input and 1.5x output for the whole request, closed weights and no fine-tuning support, and benchmark figures are vendor-reported; no SWE-bench Verified or MRCR numbers published. At $1 in / $5 out per million tokens, it sits in the budget price band.
Qwen3.8-Flash-Next: where it fits
Alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max. Released August 26, 2026 by Alibaba, it is built for sWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4), cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max, vision-based agentic tasks (AndroidWorld: 84.5), and previews Qwen4's hybrid gated-DeltaNet plus sparse-attention architecture.
Its trade-offs: trails Claude Opus 4.6 Max on Humanity's Last Exam (35.9 vs 40.0), lower OSWorld 2.0 binary success rate (19.4%), and an open-weight architecture preview rather than Alibaba's polished flagship product. At $0.16 in / $0.47 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. Qwen3.8-Flash-Next gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-6.1 Terra 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 GPT-6.1 Terra and Qwen3.8-Flash-Next 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 pricingFrequently asked questions
Is GPT-6.1 Terra or Qwen3.8-Flash-Next 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, GPT-6.1 Terra leans toward $1/$5 per million tokens (cached input $0.05) - half the price of gpt-6.1 sol and gpt-5.6 terra while Qwen3.8-Flash-Next leans toward swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-6.1 Terra or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6.1 Terra is API-metered at $1/$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?
GPT-6.1 Terra — 1.05M tokens vs 262K tokens natively (extensible to 1M with YaRN), about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-6.1 Terra and Qwen3.8-Flash-Next together?
Yes — a multi-model platform like LumiChats gives you GPT-6.1 Terra, Qwen3.8-Flash-Next 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, GPT-6.1 Terra or Qwen3.8-Flash-Next?
GPT-6.1 Terra — released October 26, 2026, about 2 months after Qwen3.8-Flash-Next.
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.