GPT-5.3 Codex vs Spark-X2.6-4B

Updated October 20, 2026. Rank says GPT-5.3 Codex is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.3 Codex has the higher public point estimate, 60.51 versus 40.14. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.3 Codex

OpenAI

60.51/100

Supported · Public rank #54

90% interval 53.2–67.8

Model B

Spark-X2.6-4B

SparkLLM

40.14/100

Estimated · Public rank #136

Conditional range 25.8–54.5

Shared results: 2
GPT-5.3 Codex only: 9
Spark-X2.6-4B only: 18
Like-for-like categories: 0 / 8

Supported: GPT-5.3 Codex · Estimated: Spark-X2.6-4B. Conditional ranges do not establish rank confidence.How the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Long documents
Prompts that approach the documented context limit
Spark-X2.6-4B
Spark-X2.6-4B has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Spark-X2.6-4B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.3 Codex and Spark-X2.6-4B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

53.7GPT-5.3 Codex30.6Spark-X2.6-4B

Directional only · BenchAlign v5.8

GPT-5.3 Codex has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

SWE-bench VerifiedCoding
Normalized gap 22.2
GPT-5.3 Codex:85%
Spark-X2.6-4B:62.8%
GPT-5.3 Codex source Spark-X2.6-4B source
SWE-bench ProCoding
Normalized gap 2.5
GPT-5.3 Codex:56.8%
Spark-X2.6-4B:54.3%
GPT-5.3 Codex source Spark-X2.6-4B source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only

GPT-5.3 Codex: 55.8; Estimated · #42/131
Spark-X2.6-4B: 33.0; Estimated · #89/131
Basis: BenchAlign v5.8 lane · 4 vs 7 public rows
Reading: Directional only

Coding

Directional only

GPT-5.3 Codex: 53.7; Supported · #34/152
Spark-X2.6-4B: 30.6; Estimated · #95/152
Basis: BenchAlign v5.8 lane · 7 vs 4 public rows
Reading: Directional only

Knowledge

Directional only

GPT-5.3 Codex: 65.4; Estimated · #33/187
Spark-X2.6-4B: 43.0; Estimated · #109/187
Basis: BenchAlign v5.8 lane · 0 vs 3 public rows
Reading: Directional only

Instruction following

Directional only

GPT-5.3 Codex: 92.6; #18/149
Spark-X2.6-4B: 78.2; #62/149
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.3 Codex: 82.2; Unranked · 2 rankable rows
Spark-X2.6-4B: 48.8; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.3 Codex: 80.1; Unranked · 1 rankable row
Spark-X2.6-4B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.3 Codex: Not ranked
Spark-X2.6-4B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.3 Codex: Not ranked
Spark-X2.6-4B: 76.4; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.3 Codex and Spark-X2.6-4B
Category	GPT-5.3 Codex	Spark-X2.6-4B	Basis	Reading
Agentic	55.8Estimated · #42/131	33.0Estimated · #89/131	Directional onlyBenchAlign v5.8 lane · 4 vs 7 public rows	Directional only
Coding	53.7Supported · #34/152	30.6Estimated · #95/152	Directional onlyBenchAlign v5.8 lane · 7 vs 4 public rows	Directional only
Knowledge	65.4Estimated · #33/187	43.0Estimated · #109/187	Directional onlyBenchAlign v5.8 lane · 0 vs 3 public rows	Directional only
Instruction following	92.6#18/149	78.2#62/149	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	82.2Unranked · 2 rankable rows	48.8Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	80.1Unranked · 1 rankable row	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	76.4Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GPT-5.3 Codex: $0.00875; Fits in one request
Spark-X2.6-4B: Self-hosted; infrastructure cost varies; Fits in one request

Spark-X2.6-4B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.3 Codex: $0.1295; Fits in one request
Spark-X2.6-4B: Self-hosted; infrastructure cost varies; Fits in one request

Spark-X2.6-4B has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-5.3 Codex: $0.525; Fits in one request; Cached input priced at the published list-input rate
Spark-X2.6-4B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate. Spark-X2.6-4B has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGPT-5.3 CodexSpark-X2.6-4B

Context window

Maximum documented context; output-token limits may be lower.

GPT-5.3 Codex

400K

OpenAI GPT-5.3 Codex model documentation

Spark-X2.6-4B

SparkLLM Spark-X2.6-4B config

GPT-5.3 Codex

Reasoning

Spark-X2.6-4B

Reasoning

Weight access

GPT-5.3 Codex

Proprietary

Spark-X2.6-4B

Open Weight

License

GPT-5.3 Codex

Proprietary

Spark-X2.6-4B

Open Weight

Release date

GPT-5.3 Codex

2026-02-05

Spark-X2.6-4B

2026-10-19

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: GPT-5.3 Codex has the higher public point estimate, 60.51 versus 40.14. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Spark-X2.6-4B has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GPT-5.3 Codex or Spark-X2.6-4B?

GPT-5.3 Codex has the higher public point estimate, 60.51 versus 40.14. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-5.3 Codex or Spark-X2.6-4B?

GPT-5.3 Codex scores higher for coding on the public lane, 53.7 to 30.6. Spark-X2.6-4B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GPT-5.3 Codex or Spark-X2.6-4B?

GPT-5.3 Codex scores higher for agentic tasks on the public lane, 55.8 to 33. GPT-5.3 Codex and Spark-X2.6-4B are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.3 Codex or Spark-X2.6-4B?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-5.3 Codex or Spark-X2.6-4B?

Spark-X2.6-4B has the larger documented context window: 1M, compared with 400K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GPT-5.3 Codex

API / mo$11,813

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Spark-X2.6-4B

API / mo$0

Self-host / mo$298

Break-even—

Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence29 rows

Agentic

Terminal-Bench 2.0
GPT-5.3 Codex77.3%
Source
Spark-X2.6-4B—
Not directly comparable
OSWorld-Verified
GPT-5.3 Codex64.7%
Source
Spark-X2.6-4B—
Not directly comparable
Gert Labs
GPT-5.3 Codex57.47%
Source
Spark-X2.6-4B—
Not directly comparable
JobBench
GPT-5.3 Codex33.7%
Source
Spark-X2.6-4B—
Not directly comparable
BFCL v4
GPT-5.3 Codex—
Spark-X2.6-4B71.8%
Source
Not directly comparable
τ²-bench results
GPT-5.3 Codex—
Spark-X2.6-4B86.9%
Source
Not directly comparable
MCP Atlas
GPT-5.3 Codex—
Spark-X2.6-4B62.4%
Source
Not directly comparable
BrowseComp
GPT-5.3 Codex—
Spark-X2.6-4B53.1%
Source
Not directly comparable
HLE w/ tools
GPT-5.3 Codex—
Spark-X2.6-4B36.9%
Source
Not directly comparable
τ³-bench results
GPT-5.3 Codex—
Spark-X2.6-4B34.7%
Source
Not directly comparable
Claw-Eval
GPT-5.3 Codex—
Spark-X2.6-4B69.9%
Source
Not directly comparable

Coding

SWE-bench Verified
GPT-5.3 Codex85%
Source
Spark-X2.6-4B62.8%
Source
GPT-5.3 Codex leads this result
SWE-bench Pro
GPT-5.3 Codex56.8%
Source
Spark-X2.6-4B54.3%
Source
GPT-5.3 Codex leads this result
SWE-Rebench
GPT-5.3 Codex58.2%
Source
Spark-X2.6-4B—
Not directly comparable
Terminal-Bench 2.0
GPT-5.3 Codex77.3%
Source
Spark-X2.6-4B—
Not directly comparable
Vibe Code Bench
GPT-5.3 Codex61.77%
Source
Spark-X2.6-4B—
Not directly comparable
LiveCodeBench (Vals)
GPT-5.3 Codex87.3%
Source
Spark-X2.6-4B—
Not directly comparable
SWE-bench (Vals)
GPT-5.3 Codex78.0%
Source
Spark-X2.6-4B—
Not directly comparable
SWE Multilingual
GPT-5.3 Codex—
Spark-X2.6-4B64.7%
Source
Not directly comparable
SciCode
GPT-5.3 Codex—
Spark-X2.6-4B36.8%
Source
Not directly comparable

Reasoning

LongBench v2
GPT-5.3 Codex—
Spark-X2.6-4B53.3%
Source
Not directly comparable

Knowledge

GPQA-D
GPT-5.3 Codex—
Spark-X2.6-4B77.1%
Source
Not directly comparable
HLE w/o tools
GPT-5.3 Codex—
Spark-X2.6-4B17.9%
Source
Not directly comparable
GPQA
GPT-5.3 Codex—
Spark-X2.6-4B69.9%
Source
Not directly comparable

Instruction following

IFEval
GPT-5.3 Codex—
Spark-X2.6-4B93.6%
Source
Not directly comparable
IFBench
GPT-5.3 Codex—
Spark-X2.6-4B75.8%
Source
Not directly comparable

Math

AIME26
GPT-5.3 Codex—
Spark-X2.6-4B91.2%
Source
Not directly comparable
HMMT Feb 2026
GPT-5.3 Codex—
Spark-X2.6-4B81.3%
Source
Not directly comparable
IMOAnswerBench
GPT-5.3 Codex—
Spark-X2.6-4B75.2%
Source
Not directly comparable

29 public results · 2 shared

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Last updated October 20, 2026

GPT-5.3 Codex vs Spark-X2.6-4B

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Instruction following

Reasoning

Multimodal

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, GPT-5.3 Codex or Spark-X2.6-4B?

Which is better for coding, GPT-5.3 Codex or Spark-X2.6-4B?

Which is better for agentic tasks, GPT-5.3 Codex or Spark-X2.6-4B?

Which costs less, GPT-5.3 Codex or Spark-X2.6-4B?

Which has the larger context window, GPT-5.3 Codex or Spark-X2.6-4B?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Instruction following

Math

Related comparisons

Watch GPT-5.3 Codex vs Spark-X2.6-4B