Model comparison
GPT-5.6 Terra vs Qwen3.8-27B
OpenAI against Qwen, compared on context, price, and verified benchmark results.
Catalog record checked August 14, 2026; individual provider fields may change.
OpenAI
GPT-5.6 Terra
Balanced
Qwen
Qwen3.8-27B
Balanced · Open weights
| Specification | GPT-5.6 Terra | Qwen3.8-27B |
|---|---|---|
| Provider | OpenAI | Qwen |
| Tier | Balanced | Balanced |
| Context window | Winner: 1.05M | 262K |
| Max output | 128K | Winner: 131K |
| Input / 1M tokens | $2 | Not verified |
| Output / 1M tokens | $12 | Not verified |
| Weights | Closed | Open |
| Parameters | Not disclosed | 27B dense |
| Reasoning levels | none, low, medium, high, xhigh, max | low, medium, xhigh |
| Modalities | text, image | text, image, video |
| Released | July 9, 2026 | August 14, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-08) | 57 | Not verified |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: GPT-5.6 Terra: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate.
GPT-5.6 Terra
OpenAI's middle tier, balancing capability against cost.
Best for
- Mixed workloads
- Teams standardising on one model
Watch out
10x Luna's input price; check whether Luna already suffices for the workload.
Qwen3.8-27B
Apache 2.0 Qwen3.8 dense VLM for local and self-hosted work — native 262K context, image and video input, thinking on by default but can be turned off. Distinct from hosted Qwen3.8-Max.
Best for
- Local multimodal agents
- Self-hosting a dense 27B VLM
- Apache 2.0 deployments
Watch out
The Hugging Face repo was created 2026-08-05; this row uses the 2026-08-14 card revision. Hosted 1M-context API is documented as coming soon. Vendor SWE-bench Pro 61.7 and DeepSWE 42.2 used a Claude Code harness, not swebench.com or the public Datacurve DeepSWE board. YaRN can extend context toward 1M.
Benchmark
DeepSWE 1.1 in context
Only GPT-5.6 Terra has a published DeepSWE 1.1 result. It is shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
GPT-5.6 Terra vs Qwen3.8-27B
Answered from the verified figures on this page rather than general guidance.
Which has the larger context window, GPT-5.6 Terra or Qwen3.8-27B?
GPT-5.6 Terra accepts 1.05M tokens against 262K for Qwen3.8-27B. This only matters if you routinely send very long documents or large codebases.
Do GPT-5.6 Terra and Qwen3.8-27B support the same reasoning levels?
GPT-5.6 Terra exposes none, low, medium, high, xhigh, max, while Qwen3.8-27B exposes low, medium, xhigh.
Should I use GPT-5.6 Terra or Qwen3.8-27B?
Both sit in the balanced tier, so the choice usually comes down to price and context rather than capability. GPT-5.6 Terra suits mixed workloads; Qwen3.8-27B suits local multimodal agents.
Can I self-host GPT-5.6 Terra or Qwen3.8-27B?
Qwen3.8-27B publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. GPT-5.6 Terra is a closed model whose supported access paths are controlled by its provider.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.