x-ai
SpaceXAI: Grok 4.3 (batch)
x-ai/grok-4.3:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
Grok 4.3 (batch), cataloged by OpenRouter as x-ai/grok-4.3:batch, is a reasoning model listed under x-ai (named SpaceXAI in the entry) that accepts text, image, and file inputs and returns text, with an advertised one-million-token context window. The catalog describes tunable reasoning effort, tool and structured-output support, and cites third-party arena and index measurements. This evidence pack contains only the catalog entry; no independent model card was available, so capabilities and the batch variant's behavior should be validated on your own workloads.[1]
Strengths
- Per OpenRouter's catalog, it offers a one-million-token context window and accepts text, image, and file inputs with text output.[1]
- Reasoning is configurable: enabled by default at low effort, with higher settings or full disablement supported.[1]
- The catalog lists tool calling, tool choice, structured outputs, seed, and response-format parameters, which suits programmatic and agentic integrations.[1]
Limitations
- Evidence is catalog-only; an independent model card was not available in this source pack, so publisher-described strengths (agentic workflows, instruction following) remain unverified and need validation on real workloads.[1]
- The meaning of the ':batch' variant is not documented in the source; batch-specific latency, availability, or behavior should be confirmed before adoption.[1]
- Knowledge cutoff is null and license terms are not stated in the supplied evidence.[1]
Where to use it
- Potentially suited to high-volume document or multimodal-to-text pipelines where deferred batch execution is acceptable; verify throughput and latency first.[1]
- A candidate for agentic and instruction-following tasks that benefit from adjustable reasoning effort and tool calling; an editorial suggestion to test, not a measured result.[1]
- Structured data extraction is plausible given the catalog's structured-outputs and response-format parameters, subject to workload testing.[1]
Sources
- [1] OpenRouter catalog · SpaceXAI: Grok 4.3 (batch)Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
Grok 4.3 is a reasoning model from SpaceXAI. It accepts text and image inputs with text output, and is suited for agentic workflows, instruction-following tasks, and applications requiring high factual...
Specifications
- Model ID
- x-ai/grok-4.3:batch
- Canonical ID
- x-ai/grok-4.3-20260430
- Creator
- x-ai
- Added to OpenRouter
- 30.04.2026
- Context length
- 1,000,000
- Modality
- text+image+file->text
- Input modalities
- text, image, file
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $1 / out $2
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | reasoning: optional · default low · efforts: high, medium, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | BB — next 30% of the cohort | — |
| Agents | BB — next 30% of the cohort | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: optional · default low · efforts: high, medium, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | input: file |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Coding index | 42.2 | BB — next 30% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 15.5 | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · agenticgamedevwin rate 28.8% · rank 25 | 955Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · agentichtmlslideswin rate 31.8% · rank 9 | 1,066Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · agenticslideswin rate 31.9% · rank 9 | 1,072Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · agenticslides(html)win rate 31.7% · rank 9 | 1,066Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · agenticslides(python-pptx)win rate 32.4% · rank 9 | 1,068Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · androidnativewin rate 22.9% · rank 43 | 955Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · blender3dwin rate 15.5% · rank 25 | 807Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · codetovideowin rate 18.3% · rank 22 | 894Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · fullstackwin rate 28% · rank 42 | 987Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · godotgamedevwin rate 23.5% · rank 36 | 920Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · htmlslideswin rate 28.6% · rank 25 | 906Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · mobileappswin rate 32.9% · rank 48 | 1,052Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · pdfwin rate 20.4% · rank 30 | 933Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · pptxslideswin rate 32.5% · rank 8 | 1,071Elo | —Not rated | 13.11.2026 |
| Design Arena | agents · python-pptxslideswin rate 30.8% · rank 27 | 1,072Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · spreadsheetwin rate 29.6% · rank 29 | 993Elo | CC — below the B band | 13.11.2026 |
| Design Arena | agents · webappswin rate 42.5% · rank 37 | 1,107Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · 3dwin rate 40.7% · rank 90 | 1,107Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · asciiartwin rate 42.2% · rank 55 | 1,122Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 43.5% · rank 69 | 1,176Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 45.1% · rank 67 | 1,177Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 42.6% · rank 71 | 1,162Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 36.8% · rank 74 | 1,073Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 45.1% · rank 69 | 1,186Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 44% · rank 71 | 1,187Elo | BB — next 30% of the cohort | 13.11.2026 |
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| xAI | 1,000,000 | $1 | $2 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.