Model comparison
Gemini 3.1 Pro vs GLM 5.3
Google against Z.ai, compared on context, price, and verified benchmark results.
Catalog record checked August 14, 2026; individual provider fields may change.
Gemini 3.1 Pro
Frontier
Z.ai
GLM 5.3
Frontier
| Specification | Gemini 3.1 Pro | GLM 5.3 |
|---|---|---|
| Provider | Z.ai | |
| Tier | Frontier | Frontier |
| Context window | Winner: 1.05M | 1M |
| Max output | 66K | Winner: 128K |
| Input / 1M tokens | $2 | Not verified |
| Output / 1M tokens | $12 | Not verified |
| Weights | Closed | Closed |
| Parameters | Not disclosed | Not disclosed |
| Reasoning levels | Not verified | low, high, max |
| Modalities | text, image, video, audio, pdf | text |
| Released | February 19, 2026 | August 14, 2026 |
| Artificial Analysis Intelligence Index (2026-08-09) | 48 | 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: Gemini 3.1 Pro: Base tier is $2/$12 per million tokens; prompts above 200K tokens are priced at $4/$18. Prefer the Preview API id `gemini-3.1-pro-preview` (not a GA alias). · GLM 5.3: Direct `glm-5.3` API is documented as coming soon and is not on the Z.ai per-token table (do not assume GLM 5.2's $1.40/$4.40). Today it is on every GLM Coding Plan: points-based quota (input / cached input / output). Off-peak is 50% of standard points. Peak is Monday–Friday 14:00–18:00 UTC+8. Coding Plan requests for GLM-5.2/5.1 are routed to 5.3.
Gemini 3.1 Pro
Google's Pro tier in limited preview since 2026-02-19, with a 1.05M-token context window and multimodal input.
Best for
- Very long documents
- Multimodal input
- Google Workspace integration
Watch out
Scored 12% on DeepSWE, far below every other model here including budget tiers — weak on agentic coding specifically. Pricing also steps up to $4/$18 above 200K tokens. Wire calls to `gemini-3.1-pro-preview`.
GLM 5.3
Z.ai's 2026-08-14 Coding Plan flagship — same base as GLM 5.2, with the documented gains from post-training only. Direct token API and open weights are not published yet.
Best for
- GLM Coding Plan agent work
- Long-horizon coding in Claude Code / OpenCode / Cline
- Teams already on Z.ai subscriptions
Watch out
Thinking cannot be disabled (`thinking.type: disabled` fails); `reasoning_effort` is low / high / max (default max). Open weights are promised about two weeks after launch pending safety review — not a downloadable checkpoint today. Vendor DeepSWE v1.1 66.9 used mini-swe-agent at 400K context, not the public Datacurve v1.1 board (snapshot generated 2026-08-13, before this launch).
Benchmark
DeepSWE 1.1 in context
Only Gemini 3.1 Pro 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
Gemini 3.1 Pro vs GLM 5.3
Answered from the verified figures on this page rather than general guidance.
Which has the larger context window, Gemini 3.1 Pro or GLM 5.3?
Gemini 3.1 Pro accepts 1.05M tokens against 1M for GLM 5.3. This only matters if you routinely send very long documents or large codebases.
Should I use Gemini 3.1 Pro or GLM 5.3?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. Gemini 3.1 Pro suits very long documents; GLM 5.3 suits glm coding plan agent work.
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.