GLM-5.5 vs GLM-5.3: What Could Change for Coding and Agents?

GLM-5.5 vs GLM-5.3 at a glance

For the source timeline, see GLM-5.5 latest news. This comparison focuses on development direction, current cost and how to evaluate a later release.

Training: post-training upgrade versus next-base-model clues

Z.ai says GLM-5.3 shares GLM-5.2’s base model. Its gains come from post-training: scaling executable work environments, improving reward verification and training on longer software-engineering tasks.[6]

The GLM-5.5 capture refers to a next-generation base model, with coding post-training and internal model merging. Lumina’s clarification says 32B / 122B / 256B are internal training models, not three upcoming public editions. The merging method and final 5.5 architecture are unspecified.

If that work reaches the release, it could extend the improvement surface beyond another tuning pass on the same base. How much it changes capabilities remains a question for released specifications and comparable evaluations.

Coding and agents: what might improve?

GLM-5.3 already targets end-to-end engineering. Its official documentation describes tasks involving code, internal documentation, experiment results and compute, with the model expected to diagnose problems, implement changes and validate results. The same documentation discusses synthesized environments, judge agents and checks against reward shortcuts.[6]

The 5.5 reports add quant-finance environments, a coding sandbox and agents generating their own training data. The RSI report says the captured profile’s “100% RSI” is an ambition. The original post calls the coding-agent work promising but unfinished.[4]

Useful improvements would show up in fewer abandoned debugging attempts, better test selection and more correct final deliveries. A stronger self-generated training loop might help those behaviors; the reports alone cannot establish the effect. Longer thinking time or a larger parameter rumor is not a measured completion-rate improvement.

Benchmarks: the GLM-5.3 baseline to keep

Z.ai publishes the following 5.3 results.[6] They are vendor-reported under the cited harnesses. There are no 5.5 scores in the retrieved sources, so a percentage lead cannot be calculated.

For a practical comparison after access opens, freeze the same repository, tool permissions, harness, reasoning budget and time limit. Record task success, hidden-test results, tool errors, total tokens, retries and wall time. Flash benchmarks should stay in a separate column because GLM-5.3-Flash is a different model from the flagship.

API prices and cost per completed task

Use the 5.3 card for today’s route quotes and the 5.5 card for future availability. At the dated check, 5.3 ranged from $0.56–$1.40 input / $1.76–$4.40 output; Flash from $0.072–$0.15 / $0.24–$0.50; FlashX from $0.148–$0.37 / $0.50–$1.25. Each minimum pair is from one active route. Cached input, tools and other charges are outside the following example.

For a hypothetical task using 100,000 uncached input and 20,000 output tokens:

The second line is 60% cheaper for identical token volume. It is arithmetic, not a measured workload. If a task took three complete attempts at that same volume each time, its 5.3 token cost would be $0.2736. A future 5.5 price would need to be judged against success rate and retry volume as well as token rates.

Which model should you choose, and what should you test?

Choose 5.3 for an existing text-based coding integration. Consider Flash for native visual work or lower token cost, and FlashX when the serving-speed tradeoff matters. A sparse OpenCode 5.5-Flash listing is a release clue rather than a deployable API specification.

Prepare these proposed tests for 5.5 access; we have not run them on 5.5:

1. Repository repair: give both models a pinned multi-file project with a retry bug. Require a minimal patch, regression checks and a successful existing test suite. Judge the hidden failure case, not the explanation.
2. Finance coding: supply a fixed dataset with missing dates and a requirement to avoid look-ahead bias. Require executable code, validation of train/test boundaries and a reproducible report. This probes the reported environment direction without assuming specialist accuracy.
3. Agent recovery: make one tool response incomplete and one test fail. Check whether the agent inspects the failure, changes its approach and completes the original task within the same budget.

These tasks turn the reported training directions into observable behaviors. Reuse them unchanged for 5.3 and the eventual 5.5 endpoint.

Start a GLM-5.3 baseline through APIMaster

Register, create an API key, and configure https://apimaster.ai/v1 with glm-5.3. The documented model requires reasoning enabled; do not carry a disabled-thinking setting into the request.

curl "https://apimaster.ai/v1/chat/completions" \
  -H "Authorization: Bearer $APIMASTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"glm-5.3","thinking":{"type":"enabled"},"reasoning_effort":"max","messages":[{"role":"user","content":"Inspect this bug report, propose a patch, and include tests that verify the fix."}]}'

Keep the model ID configurable and save your evaluation artifacts. The checked starting 5.3 route is up to 60% below official uncached rates; GLM-5.5 integration details can be added when an actual route is available.

FAQ

Is GLM-5.5 better than GLM-5.3 at coding?

The reported training direction suggests coding is a focus, but no comparable 5.5 results were found in the cited sources. A measured lead cannot yet be stated.

Does GLM-5.5 have a bigger context window?

Its context and output limits are not supplied. The documented 5.3 baseline is 1M context and 128K output.

Are 32B, 122B and 256B the GLM-5.5 public versions?

Lumina clarified that these are internal post-training models being merged into the next base model.

Is GLM-5.5 cheaper than GLM-5.3?

No public 5.5 price is available in these sources. APIMaster’s checked 5.3 starting pair is $0.56 / $1.76 per million input / output tokens.

Should I wait for GLM-5.5?

For work today, use an available model and establish a reproducible baseline. Follow 5.5 for the reported next-base-model, coding and RSI direction; keep release-date expectations separate from confirmed API access.

Sources

Checked November 1, 2026. Proposed evaluations and cost examples are not 5.5 test results.

1. Lumina: GLM-5.5 post-training original post.
2. LuminaBench: preserved profile capture and post-training report.
3. Lumina: internal-model clarification.
4. Lumina: agents generating training data, and RSI report.
5. OpenCode: GLM-5.5-Flash data.
6. Z.ai: GLM-5.3 documentation.
7. Z.ai: GLM-5.3-Flash documentation.
8. Z.ai: official pricing.
9. APIMaster live marketplace, November 1 at 21:24 UTC+8: 5.5, 5.5 Flash, 5.3, Flash, FlashX.