Model comparison
GPT-5.6 Terra vs Muse Glimmer 30B
OpenAI against Meta, compared on context, price, and verified benchmark results.
Catalog record checked August 12, 2026; individual provider fields may change.
OpenAI
GPT-5.6 Terra
Balanced
Meta
Muse Glimmer 30B
Balanced · Open weights
| Specification | GPT-5.6 Terra | Muse Glimmer 30B |
|---|---|---|
| Provider | OpenAI | Meta |
| Tier | Balanced | Balanced |
| Context window | Winner: 1.05M | 131K |
| Max output | 128K | Not verified |
| Input / 1M tokens | $2 | Winner: $0 |
| Output / 1M tokens | $12 | Winner: $0 |
| Weights | Closed | Open |
| Parameters | Not disclosed | ~29.6B dense (incl. ~1.8B perception encoder) |
| Reasoning levels | none, low, medium, high, xhigh, max | Not verified |
| Modalities | text, image | text, image |
| Released | July 9, 2026 | August 10, 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. · Muse Glimmer 30B: Apache 2.0 open weights (BF16 + official GGUF); hosted inference billed by provider.
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.
Muse Glimmer 30B
Meta's open-weight multimodal agentic model distilled from Muse Spark for local consumer hardware (~24–32GB class with 4-bit).
Best for
- Local agents
- On-device coding + tool use
- Privacy-sensitive multimodal work
Watch out
Full BF16 needs far more than 24GB — plan on official GGUF / 4-bit packs (and mmproj for vision). Agentic quality ≠ frontier Muse Spark API; validate on your workflows.
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 Muse Glimmer 30B
Answered from the verified figures on this page rather than general guidance.
Is GPT-5.6 Terra or Muse Glimmer 30B cheaper for input?
Muse Glimmer 30B is cheaper at $0 per million input tokens, against $2 for GPT-5.6 Terra. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.6 Terra has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate. Muse Glimmer 30B has tiered pricing: Apache 2.0 open weights (BF16 + official GGUF); hosted inference billed by provider.
Is GPT-5.6 Terra or Muse Glimmer 30B cheaper for output?
Muse Glimmer 30B is cheaper at $0 per million output tokens, against $12 for GPT-5.6 Terra. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-5.6 Terra has tiered pricing: Prompts above 272K tokens bill at 2× input and 1.5× output; cache writes are 1.25× the input rate. Muse Glimmer 30B has tiered pricing: Apache 2.0 open weights (BF16 + official GGUF); hosted inference billed by provider.
Which has the larger context window, GPT-5.6 Terra or Muse Glimmer 30B?
GPT-5.6 Terra accepts 1.05M tokens against 131K for Muse Glimmer 30B. This only matters if you routinely send very long documents or large codebases.
Should I use GPT-5.6 Terra or Muse Glimmer 30B?
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; Muse Glimmer 30B suits local agents.
Can I self-host GPT-5.6 Terra or Muse Glimmer 30B?
Muse Glimmer 30B 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.