Pick GPT-6 Luna for openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume or available to free and go plan users in the chatgpt desktop app, not gated to paid tiers. Pick Reka Flash 3.1 for a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists) or strong base for fine-tuning agentic tasks - notably improved coding since the original flash 3. Choose Reka Flash 3.1 if you need self-hosting or data privacy; GPT-6 Luna if you want a managed API.
GPT-6 Luna (OpenAI) and Reka Flash 3.1 (Reka AI) are two of the models people most often weigh against each other in 2026. GPT-6 Luna is openAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Reka Flash 3.1 is reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Reka Flash 3.1 ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-6 Luna is API-metered at $0.1/$0.5 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: GPT-6 Luna holds 32× more — 1.05M tokens (~1,575 pages) vs 32K (~49 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: GPT-6 Luna is the newer model by about 15 months (released September 22, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GPT-6 Luna
Reka Flash 3.1
Provider
OpenAI (US)
Reka AI (US)
Released
September 22, 2026
July 2025
Context window
1.05M tokens (~1,575 pages)
32K (~49 pages)
Price (in/out)
$0.1/$0.5 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
OpenAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume: GPT-6 Luna — Its 1.05M tokens window holds about 32× more than Reka Flash 3.1's 32K in a single prompt.
Available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers: GPT-6 Luna — OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it carries the larger 1.05M tokens context.
Cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family: GPT-6 Luna — OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it is the newer of the two.
A 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists): Reka Flash 3.1 — Open weights make this possible at all — GPT-6 Luna is API-only, so it cannot leave the vendor's servers.
Strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3: Reka Flash 3.1 — Reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni — and its weights are open while GPT-6 Luna is API-only.
Fully open weights (Apache 2.0) from a frontier-caliber research team: Reka Flash 3.1 — Reka Flash 3.1 lists fully open weights (Apache 2.0) from a frontier-caliber research team among its strengths; GPT-6 Luna does not.
Lowest cost at scale: Reka Flash 3.1 — Its weights are open, so at volume you pay for your own hardware instead of GPT-6 Luna's $0.1/$0.5 per 1M tokens.
Largest single-prompt input: GPT-6 Luna — Its 1.05M tokens window is about 32× larger than Reka Flash 3.1's 32K, fitting roughly 1,575 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Reka Flash 3.1 — At Open weight (self-host / free) it undercuts GPT-6 Luna, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GPT-6 Luna — Larger 1.05M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs: Reka Flash 3.1 — Open weights let you run it on your own hardware; GPT-6 Luna is API-only.
Anyone whose priority is openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume: GPT-6 Luna — It is specifically built for that.
Anyone whose priority is a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists): Reka Flash 3.1 — That is its strongest area.
GPT-6 Luna: where it fits
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Released September 22, 2026 by OpenAI, it is built for openAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers, cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family, and 1.05M-token context window carried down from the flagship tier.
Its trade-offs are real: less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems, and as the entry-level tier, expect it to be the first model swapped out when OpenAI ships the next cost-tier refresh. At $0.1 in / $0.5 out per million tokens, it sits in the budget price band.
Reka Flash 3.1: where it fits
Reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni. Released July 2025 by Reka AI, it is built for a 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists), strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3, fully open weights (Apache 2.0) from a frontier-caliber research team, and built by alumni from Google DeepMind and Meta's AI labs.
Its trade-offs: a relatively small 32K context window next to million-token frontier models, smaller and less capable overall than flagship frontier models from major labs, and reka's broader roadmap has shifted toward robotics/world models after merging with Moonvalley in June 2026, raising questions about ongoing LLM investment. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
The defining split here is open vs. closed. Reka Flash 3.1 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-6 Luna gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is GPT-6 Luna or Reka Flash 3.1 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, GPT-6 Luna leans toward openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume while Reka Flash 3.1 leans toward a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-6 Luna or Reka Flash 3.1?
Reka Flash 3.1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Luna is API-metered at $0.1/$0.5 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
GPT-6 Luna — 1.05M tokens vs 32K, about 32× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-6 Luna and Reka Flash 3.1 together?
Yes — a multi-model platform like LumiChats gives you GPT-6 Luna, Reka Flash 3.1 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GPT-6 Luna or Reka Flash 3.1?
GPT-6 Luna — released September 22, 2026, about 15 months after Reka Flash 3.1.
GPT-6 Luna vs Reka Flash 3.1
OpenAI · US | Reka AI · US · Updated June 2026
Quick verdict
Pick GPT-6 Luna for openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume or available to free and go plan users in the chatgpt desktop app, not gated to paid tiers. Pick Reka Flash 3.1 for a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists) or strong base for fine-tuning agentic tasks - notably improved coding since the original flash 3. Choose Reka Flash 3.1 if you need self-hosting or data privacy; GPT-6 Luna if you want a managed API.
GPT-6 Luna (OpenAI) and Reka Flash 3.1 (Reka AI) are two of the models people most often weigh against each other in 2026. GPT-6 Luna is openAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Reka Flash 3.1 is reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: Reka Flash 3.1 ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-6 Luna is API-metered at $0.1/$0.5 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: GPT-6 Luna holds 32× more — 1.05M tokens (~1,575 pages) vs 32K (~49 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: GPT-6 Luna is the newer model by about 15 months (released September 22, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GPT-6 Luna
Reka Flash 3.1
Provider
OpenAI (US)
Reka AI (US)
Released
September 22, 2026
July 2025
Context window
1.05M tokens (~1,575 pages)
32K (~49 pages)
Price (in/out)
$0.1/$0.5 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
OpenAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume
GPT-6 Luna
Its 1.05M tokens window holds about 32× more than Reka Flash 3.1's 32K in a single prompt.
Available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers
GPT-6 Luna
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it carries the larger 1.05M tokens context.
Cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family
GPT-6 Luna
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it is the newer of the two.
A 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists)
Reka Flash 3.1
Open weights make this possible at all — GPT-6 Luna is API-only, so it cannot leave the vendor's servers.
Strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3
Reka Flash 3.1
Reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni — and its weights are open while GPT-6 Luna is API-only.
Fully open weights (Apache 2.0) from a frontier-caliber research team
Reka Flash 3.1
Reka Flash 3.1 lists fully open weights (Apache 2.0) from a frontier-caliber research team among its strengths; GPT-6 Luna does not.
Lowest cost at scale
Reka Flash 3.1
Its weights are open, so at volume you pay for your own hardware instead of GPT-6 Luna's $0.1/$0.5 per 1M tokens.
Largest single-prompt input
GPT-6 Luna
Its 1.05M tokens window is about 32× larger than Reka Flash 3.1's 32K, fitting roughly 1,575 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Reka Flash 3.1
At Open weight (self-host / free) it undercuts GPT-6 Luna, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-6 Luna
Larger 1.05M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Reka Flash 3.1
Open weights let you run it on your own hardware; GPT-6 Luna is API-only.
Anyone whose priority is openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume
→ GPT-6 Luna
It is specifically built for that.
Anyone whose priority is a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists)
→ Reka Flash 3.1
That is its strongest area.
GPT-6 Luna: where it fits
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Released September 22, 2026 by OpenAI, it is built for openAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers, cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family, and 1.05M-token context window carried down from the flagship tier.
Its trade-offs are real: less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems, and as the entry-level tier, expect it to be the first model swapped out when OpenAI ships the next cost-tier refresh. At $0.1 in / $0.5 out per million tokens, it sits in the budget price band.
Reka Flash 3.1: where it fits
Reka AI's compact 21B open-weight reasoning model - small enough to run locally, from a team of DeepMind and Meta alumni. Released July 2025 by Reka AI, it is built for a 21B-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists), strong base for fine-tuning agentic tasks - notably improved coding since the original Flash 3, fully open weights (Apache 2.0) from a frontier-caliber research team, and built by alumni from Google DeepMind and Meta's AI labs.
Its trade-offs: a relatively small 32K context window next to million-token frontier models, smaller and less capable overall than flagship frontier models from major labs, and reka's broader roadmap has shifted toward robotics/world models after merging with Moonvalley in June 2026, raising questions about ongoing LLM investment. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
The defining split here is open vs. closed. Reka Flash 3.1 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-6 Luna gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both GPT-6 Luna and Reka Flash 3.1 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Is GPT-6 Luna or Reka Flash 3.1 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, GPT-6 Luna leans toward openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume while Reka Flash 3.1 leans toward a 21b-parameter reasoning model, small enough for local/on-device deployment (a 3.5-bit quantized build exists), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GPT-6 Luna or Reka Flash 3.1?
Reka Flash 3.1 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Luna is API-metered at $0.1/$0.5 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
GPT-6 Luna — 1.05M tokens vs 32K, about 32× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-6 Luna and Reka Flash 3.1 together?
Yes — a multi-model platform like LumiChats gives you GPT-6 Luna, Reka Flash 3.1 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GPT-6 Luna or Reka Flash 3.1?
GPT-6 Luna — released September 22, 2026, about 15 months after Reka Flash 3.1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.